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174 results for “Biodiversity assessment”
Summary for policymakers of the assessment report on land degradation and restoration of the Intergovernmental SciencePolicy Platform on Biodiversity and Ecosystem Services: Figure SPM.1
<p>The purpose of Figure SPM.1 is to support the statement that land degradation occurs just about everywhere in the world (i.e. it is ‘pervasive’), takes many forms, and that examples of successful restoration are also widespread. The figure consists of a backdrop map of the world from a multiple land degradation perspective, showing the level of uncertainty between studies, overlaid with dots representing all the places specifically mentioned in the eight chapters of the main Assessment Report on Land Degradation and Restoration, including case studies of both degradation and restoration. Around the map are brief notes regarding the main forms of degradation encountered.</p>
Fig. 2. Lissamphibian biodiversity through time. A in Assessing confidence intervals for stratigraphic ranges of higher taxa: The case of Lissamphibia
Fig. 2. Lissamphibian biodiversity through time. A. Number of species found in each geologic stage. B. Standardized species numbers, calculated to account for uneven stage durations. C. Number of lineages, obtained by adding the number of recorded species and the number of ghost lineages for each geologic stage. Major biological crises identified in other taxa are shown as continuous gray lines. Minor crises that may have affected lissamphibians are shown as dashed gray lines. Since most post−Miocene species are excluded, this figure ends with the terminal Miocene. Minimum (white) and maximum (black) values have been calculated under various assumptions about the age of several middle Miocene species and the status of specimens with questionable affinities. The geologic timescale follows Gradstein et al. (2004) in all figures. Abbreviations: J, Jurassic; K, Cretaceous; M, Miocene; O, Oligocene; P, Permian; Pg, Paleogene; Tr, Triassic.
D6.3 - Outputs of the assessment of the biodiversity loss induced by the soy commodities market between Brazil and the European Union
<p><span>This dataset compiles the results of an assessment of biodiversity loss caused by the soy commodities trade between Brazil and the European Union from 2004 to 2020. The file “SM” refers to supplementary material. The file “POA1” contains biodiversity loss characterisation factors for land occupation and land transformation, estimated based on CLEVER D2.4. The file “POA2” includes indicators of biodiversity loss due to soy cultivation in Brazil at the municipality level. Finally, the file “POA3” presents the biodiversity footprint of the Brazilian soy commodities traded in the European Union.</span></p>
Data for 'A multi‐methods approach for assessing how conserving biodiversity interacts with other sustainable development goals in Nepal'
<p>Title: Data for 'A multi-methods approach for assessing how conserving biodiversity interacts with other sustainable development goals in Nepal'</p> <p>Recommended citation: Adhikari, B., Urbach, D., Chettri, N., Sharma, E., Breu, T., Geschke, J., Fischer, M. & Prescott, G. W. (2023) A multi-methods approach for assessing how conserving biodiversity interacts with other sustainable development goals in Nepal. Sustainable Development https://doi.org/10.1002/sd.2582</p> <p>R code available at: https://github.com/biraj-ad/SDGIntearctions_Nepal_2023</p> <p>Principal Investigator: Graham W Prescott (graham.prescott.research@gmail.com)</p> <p>Authors:<br> Biraj Adhikari (biraj.adhikari@unibe.ch, ORCID: 0000-0002-4260-8706)<br> Davnah Urbach (davnah.payne@unibe.ch, ORCID: 0000-0001-9170-7834)<br> Nakul Chettri (nakul.chettri@icimod.org, ORCID: 0000-0002-3338-8879)<br> Eklabya Sharma (eklabya.sharma11@gmail.com, ORCID:0000-0003-3089-8838)<br> Thomas Breu (thomas.breu@unibe.ch, ORCID: 0000-0003-2348-504X)<br> Jonas Geschke (jonas.geschke@unibe.ch, ORCID: 0000-0002-5654-9313)<br> Markus Fischer (markus.fischer@ips.unibe.ch, ORCID: 0000-0002-5589-5900)<br> Graham W Prescott (graham.prescott.research@gmail.com, ORCID:0000-0001-5123-514X)</p> <p>Date of data collection: August 2021 - June 2022<br> Location of data collection: Kathmandu<br> Date of final release:</p> <p>Data Overview:<br> There are two excel files. Each excel sheet is also uploaded as a separate .csv file.</p> <p>1. 'Alldata.xls': This excel file contains data collected for three independent methods used to develop the study. There are four sheets in this excel workbook. The first three sheet pertains to data collected for the seven-point SDG interactions score, correlation, and expert elicitation method. The last sheet is a derivative of the first three methods, which contains synthesized information of data.<br> Sheet overview:<br> method1: This sheet relates to the data collected through online expert survey using Kobo Toolbox. Description of columns:<br> institution -> institutional affiliation of the participant<br> inst_cat -> categorization of the institution into Intergovernmental Organization (IGO), Non-governmental Organization (NGO), Academia, and Government.<br> exp_years -> experience (in years) of the participant in the conservation sector of Nepal<br> sdgout and sdgin -> The SDG that the participant was randomly assigned to rate its outgoing and incoming interaction with SDG 15 respectively<br> outscore -> the outgoing interaction score assigned by the participant, consisting of values between -3 (Cancelling) to +3 (Indivisible)<br> conf_out -> the degree of confidence of the participant in their answer to the outgoing interaction score<br> text_out -> (optional) a description of why the participant gave that outgoing interaction score<br> inscore -> the incoming interaction score assigned by the participant, consisting of values between -3 (Cancelling) to +3 (Indivisible)<br> conf_in -> the degree of confidence of the participant in their answer to the incoming interaction score<br> text_in -> (optional) a description of why the participant gave that incoming interaction score</p> <p>method2: This sheet relates to the data collected for correlation analysis. This includes time-series data of SDG indicators for Nepal obtained from the Global SDG Indicators Database (https://unstats.un.org/sdgs/indicators/database/). Description of columns:<br> Year -> Year when the indicator was measured<br> forest_cover -> Forest cover as a percent of total area (%)<br> kba_freshwater -> Average proportion of Freshwater Key Biodiversity Areas (KBAs) covered by protected areas (%)<br> kba_terrestiral -> Average proportion of Terrestrial Key Biodiversity Areas (KBAs) covered by protected areas (%)<br> redlist -> Red List Index<br> undernourishment -> Prevalence of undernourishment (%)<br> food_insecurity -> Prevalence of moderate or severe food insecurity in the adult population (%)<br> death_chronic -> Mortality rate attributed to cardiovascular disease, cancer, diabetes or chronic respiratory disease (probability)<br> suicide -> Suicide mortality rate (deaths per 100,000 population)<br> uhc_index -> Universal health coverage (UHC) service coverage index<br> f_parliament -> Proportion of seats held by women in national parliaments (% of total number of seats)<br> safewater -> Proportion of population using safely managed drinking water services (%)<br> electricity -> Proportion of population with access to electricity, by urban/rural (%)<br> cleanfuel -> Proportion of population with primary reliance on clean fuels and technology (%)<br> renewable -> Renewable energy share in the total final energy consumption (%)<br> gdp_capita -> Annual growth rate of real GDP per capita (%)<br> manu_value -> Manufacturing value added as a proportion of GDP (%)<br> consumption_gdp -> Domestic material consumption per unit of GDP (kilograms per constant 2010 United States dollars)<br> consumption_all -> Domestic material consumption (tonnes)<br> consumption_cap -> Domestic material consumption per capita (tonnes)<br> death_missing -> Number of deaths, missing persons and persons affected by disaster per 100,000 people<br> local_drr -> Proportion of local governments that adopt and implement local disaster risk reduction strategies in line with national disaster risk reduction strategies (%)</p> <p>method3: This sheet relates to the data collected through key informant interviews. We obtained the count of interactions by coding interview responses as outgoing or incoming co-benefits or trade-offs in MaxQDA. The sheet summarizes the count for type of interaction (co-benefit or trade-off) for each SDG.</p> <p>allmethods: This sheet summarizes the proportion of co-benefits or trade-offs uncovered by each method for each SDG.</p> <p>2. 'ForSynthesistable.xlsx': This excel file synthesizes goal level interactions for the three methods in three separate sheets. We use this table to develop figure 6 of the Manuscript.</p> <p> </p>
Integration of environmental DNA metabarcoding technique to reinforce fish biodiversity assessments in seagrass ecosystems: A case study of Gazi Bay Seagrass meadows
<p><span>Assessing biodiversity in marine nearshore ecosystems is crucial for effective management, especially in the context of climate change and overexploitation of marine resources. Conventional methods often fall short in providing comprehensive information for managing seagrass ecosystems. However, the emergence of environmental DNA (eDNA) techniques has transformed the field by enabling non-invasive surveys that are cost-effective and provide detailed information with high resolution. In this study, we utilized eDNA to assess fish diversity and compared its effectiveness to conventional techniques such as catch assessment surveys and underwater surveys. </span>We sampled three habitats (A: mangrove-seagrass, B: seagrass only, and C: coral-seagrass) with 4 replicates. Site A recorded 8 fish species, site B had 16 species, and site C, characterized by coral and seagrass habitats, exhibited the highest fish diversity with 45 species (mean H' index = 2.455), underscoring its ecological importance. To ensure accurate taxonomic identification, we utilized an updated MiFish reference database containing a larger number of fish species compared to the initial library. This expanded reference database with 9,569 fish species, facilitated more precise identification and enhanced the reliability of our findings. Notably, the eDNA technique outperformed conventional methods by detecting 23 additional fish species that went undetected using traditional surveys. Moreover, our study documented five fish species previously unknown to occur within the study region, further emphasizing the value of eDNA analysis in uncovering hidden biodiversity. These findings strongly advocate for integrating eDNA techniques into the monitoring and assessment of biodiversity in shallow tropical habitats of the Western Indian Ocean. By leveraging eDNA surveys, we can gain valuable insights into fish diversity, discover hidden species, and make informed decisions for the conservation and management of these ecologically significant areas.</p>
A practice-led assessment of landscape restoration potential in a biodiversity hotspot
Open the record for dataset details and reuse information.
Data for: Revealing hidden sources of uncertainty in biodiversity trend assessments
Open the record for dataset details and reuse information.
Integration of environmental DNA metabarcoding technique to reinforce fish biodiversity assessments in seagrass ecosystems: A case study of Gazi Bay Seagrass meadows
Open the record for dataset details and reuse information.
Data from: Biodiversity assessment of tropical shelf eukaryotic communities via pelagic eDNA metabarcoding
<p>Our understanding of marine communities and their functions in an ecosystem relies on the ability to detect and monitor species distributions and abundances. Currently, the use of environmental DNA (eDNA) metabarcoding is increasingly being applied for the rapid assessment and monitoring of aquatic species. Most eDNA metabarcoding studies have either focused on the simultaneous identification of a few specific taxa/groups or have been limited in geographical scope. Here we employed eDNA metabarcoding to compare beta diversity patterns of complex pelagic marine communities in tropical coastal shelf habitats spanning the whole Caribbean Sea. We screened 68 water samples using a universal eukaryotic COI barcode region and detected highly diverse communities, which varied significantly among locations, and proved good descriptors of habitat type and environmental conditions. Less than 15% of eukaryotic taxa were assigned to metazoans, most DNA sequences belonged to a variety of planktonic 'protists', with over 50% of taxa unassigned at the phylum level, suggesting that the sampled communities host an astonishing amount of micro-eukaryotic diversity yet undescribed or absent from COI reference databases. Although such a predominance of micro-eukaryotes severely reduces the efficiency of universal COI markers to investigate vertebrate and other metazoans from aqueous eDNA, the study contributes to the advancement of rapid biomonitoring methods, and brings us closer to a full inventory of extant marine biodiversity.</p>
Data from: Comparison of rapid biodiversity assessment of meiobenthos using MALDI-TOF MS and metabarcoding
<p>Nowadays, most biodiversity assessments involving meiofauna are mainly carried out using very time-consuming, specimen-wise morphological identifications, which demands comprehensive taxonomic knowledge. Animals have to be examined for minor differences of setae compositions, mouthpart morphology or number of segments for various extremities. DNA-based methods such as metabarcoding as well as recently emerged rapid analyses using MALDI-TOF mass spectrometry to identify specimens based on a proteome fingerprint could vastly accelerate the process of specimen identification in biodiversity assessments. However, these techniques depend on reference libraries to connect collected data to morphologically described species. In this study the success rate of both approaches have been tested based on reference libraries constructed using part of the samples from a new study area to identify unknown samples. Using MALDI-TOF MS we found, that species which do not exist in an incomplete mass spectra reference library only have minor impact on the results, when employing a post hoc test for Random Forest classifications. This test reveals specimens that demand morphological re-examination for the final species assignment. Metabarcoding however strongly demands a rich reference library to provide correct MOTU assessments in congruence with morphological determination. Nevertheless, with a complete library and a suitable data transformation [herein log(x + 1)], the number of reads per MOTU reflects relative species abundances in metabarcoding inference. The results of this study facilitate specimen identification by using MALDI-TOF MS, which is incomparably cheap for specimen-by specimen identification, but when it comes to sample-wise analyses, metabarcoding outperforms other techniques by far.</p>
Figure 4. A in Avian biodiversity assessment studies in a Neotropical wetland - the combination of sampling methods makes the difference
Figure 4. A Song Meter (acoustic recorder, upper left, marked yellow) and a camera trap (photo and video recorder, lower left, marked green) were jointly applied to study the avian occurrence and behavior of ground-dwelling species, e.g., cracids and tinamous.
Figure 1 in Avian biodiversity assessment studies in a Neotropical wetland - the combination of sampling methods makes the difference
Figure 1. Number of individuals and species detected by three methodologies over the dry period of 2014 in areas of forest and savanna in the northeastern Pantanal, Mato Grosso, Brazil. MN = mist nets; PC = point counts; ARU = autonomous acoustic recording.
Figure 3 in Avian biodiversity assessment studies in a Neotropical wetland - the combination of sampling methods makes the difference
Figure 3. Local contribution to beta diversity (LCBD) of avian taxa in the northeastern Pantanal, Mato Grosso, Brazil, in the dry period of 2014. The geographic positions of the savanna sites (orange) and forest sites (green) are shown. The relationship between the LCBD values and species richness is shown in A. The site contributions to beta diversity (i.e., the LCBD values) determined by each methodology were divided into two components: replacement B and nestedness C.
Figure 2 in Avian biodiversity assessment studies in a Neotropical wetland - the combination of sampling methods makes the difference
Figure 2. Number of species detected in each of the eight sampling sites using three methodologies over the dry period of 2014 in one region of the northeastern Pantanal, Mato Grosso, Brazil. MN = mist nets; PC = point counts; ARU = autonomous acoustic recording.
Resolved EXiobase (REX II) with regionalized biodiversity loss impact assessment of global mining – second version of a highly-resolved MRIO database for the year 2014
<p>This repository provides a new version of the highly-resolved global multi-regional input-output database called REX II (Resolved EXiobase) for the year 2014 with improved data quality for all mining and metals processing sectors, including a regionalized biodiversity impact assessment for all mining sectors. This regionalized impact assessment is based on the global mining area data set of Maus et al (2020). The database REX II is described in the study <em>"Hotspots of mining-related biodiversity loss in global supply chains and the potential for reduction by renewable electricity".</em></p> <p>Study: <a href="https://doi.org/10.1021/acs.est.2c04003">https://doi.org/10.1021/acs.est.2c04003</a></p> <p>Open-access preprint: <a href="https://doi.org/10.31223/X5T064">https://doi.org/10.31223/X5T064</a></p> <p> </p> <p>An earlier version of this database (REX I) with time series from 1995–2015 is provided under: <a href="http://doi.org/10.5281/zenodo.3993659">http://doi.org/10.5281/zenodo.3993659</a> and described here: <a href="https://doi.org/10.1016/j.scitotenv.2020.142587">https://doi.org/10.1016/j.scitotenv.2020.142587</a></p> <p> </p> <p>The repository REXIA_2014 contains the following files (<em>*.mat-files</em>) referring to the year 2014:<br> T_REXIA: transaction matrix<br> Y_REXIA: final demand matrix<br> Ext_REXIA and Ext_hh_REXIA: the satellite matrices of the economy and the final demand<br> The labels of all matrices are described in the excel file Labels_REXIA.xlsx</p>
Assessing the effects of artificial light at night on biodiversity across latitude – Current knowledge gaps
<p><span><b>Aim: </b>Exposure to artificial light at night (ALAN) is a risk factor for organisms. Considering the spread and increasing intensity of night brightness across the globe, and the key role of light at all biological levels, alterations of ecosystems are expected. Yet, we cannot predict the severity of the effects of ALAN in several biomes because little information is available outside the temperate zone. We reviewed current knowledge and identified traits that could be targeted to fill this knowledge gap in order to contribute to the elaboration of a biogeographical framework for the study of ALAN at the global scale.</span></p> <p><span><b>Location: </b>global.</span></p> <p><span><b>Time period: </b>current and next decades.</span></p> <p><span><b>Method: </b>We analysed the latitudinal variation of ALAN and focused on environmental factors that vary with latitude but that have been overlooked. We reviewed biological traits that exhibit latitudinal variation and depend on light and photoperiod and compiled information about the predicted changes of human demography and road networks across different world regions. </span></p> <p><span><b>Results:</b> Cloud cover amplifies ALAN far away from urbanized areas. Because of the higher frequency of overcast sky nights, exposure effects may be stronger both at high latitudes and across a large fraction of the intertropical zone, though at different times of the year. Intertropical biomes host the largest fraction of global biodiversity. Although currently they are not the most exposed to ALAN, their human populations are growing, and urbanized areas and road networks are expanding. Hence, ALAN could have strong ecological consequences, with cloud cover as an aggravating factor.</span></p> <p><span><b>Perspectives: </b>Knowledge gaps currently limit our ability to predict the effects of ALAN in different biomes. Therefore, it will be important to start investigating the consequences of this novel environmental factor across the globe, in order to develop a relevant theoretical framework.</span></p>
Data from: Proteomic fingerprinting enables quantitative biodiversity assessments of species and ontogenetic stages in Calanus congeners (Copepoda, Crustacea) from the Arctic Ocean
<p><span>Species identification is pivotal in biodiversity assessments, and proteomic fingerprinting by MALDI-TOF mass spectrometry has already been shown to reliably identify calanoid copepods to species level. However, MALDI-TOF data may contain more information beyond mere species identification. In this study, we investigated different ontogenetic stages (copepodids C1-C6 females) of three co-occurring <em>Calanus</em> species from the Arctic Fram Strait, which cannot be identified to species level based on morphological characters alone. Differentiation of the three species based on mass spectrometry data was without any error. In addition, a clear stage-specific signal was detected in all species, supported by clustering approaches as well as machine learning using Random Forest. More complex mass spectra in later ontogenetic stages as well as relative intensities of certain mass peaks were found as the main drivers of stage distinction in these species. Through a dilution series, we were able to show that this did not result from the higher amount of biomass that was used in tissue processing of the larger stages. Finally, the data were tested in a simulation for application in a real biodiversity assessment by using Random Forest for stage classification of specimens absent from the training data. This resulted in a successful stage-identification rate of almost 90%, making proteomic fingerprinting a promising tool to investigate polewards shifts of Atlantic <em>Calanus</em> species and, in general, to assess stage compositions in biodiversity assessments of Calanoida, which can be notoriously difficult using conventional identification methods.</span></p>
Fig. 4 in Amphibian diversity of a West African biodiversity hotspot: an assessment and commented checklist of the batrachofauna of the Ivorian part of the Nimba Mountains
Fig. 4. Lower to mid-elevation dense, evergreen forest (habitat C) at 847 m asl.
Fig. 3 in Amphibian diversity of a West African biodiversity hotspot: an assessment and commented checklist of the batrachofauna of the Ivorian part of the Nimba Mountains
Fig. 3. Mid-elevation savannah (habitat B).
Fig. 2 in Amphibian diversity of a West African biodiversity hotspot: an assessment and commented checklist of the batrachofauna of the Ivorian part of the Nimba Mountains
Fig. 2. Montane grasslands (habitat A) at 1,241 m asl.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.