Global Species Abundance and Diversity

Ecological Insights for the Anthropocene

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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...

Carbon sequestration and biodiversity are tightly linked, but many models projecting carbon storage change do not account for the role biodiversity plays in the sequestration capacity of terrestrial ecosystems. Here, we link a macroecological model projecting changes in vascular plant richness with empirical biodiversity-biomass stock relationships, to assess the consequences of plant biodiversity loss for carbon storage under multiple climate and land-use change scenarios. Data presented here include global raster files of plant species loss by ecoregion, biomass loss by ecoregion, and carbon loss by ecoregion. Estimates are what is expected over the long term, when ecosystems approach their new equilibrium states, based on climate and land-use changes projected for 2050.This data release is associated with the publication Biodiversity loss reduces global terrestrial carbon storage published in Nature Communications.

Animal populations worldwide have fallen an average of 73 percent from 1970 to 2020. The most dramatic decline has been experienced in Latin America and the Caribbean, where the Living Planet Index (LPI) dropped by 95 percent in the past five decades. Biodiversity around the world is under threat from human activities such as changes in land use. Humankind's impact on climate change is also having an effect on these delicate ecosystems.

Code scripts for analysis for the manuscript, "Global Biodiversity Loss from Outsourced Deforestation," including a readme.md file. All files can also be found at the github repository: github.com/AlexWiebe/Outsourced-Biodiversity-Loss. Also attached is a .zip folder of processed species-level data. Please contact with any questions about code or data availability.

The global Living Planet Index continues to decline. It shows an average 68% decrease in population sizes of mammals, birds, amphibians, reptiles and fish between 1970 and 2016. A 94% decline in the LPI for the tropical sub-regions of the Americans is the largest fall observed in any part of the world. It matters because biodiversity is fundamental to human life on Earth, and the evidence is unequivocal - it is being destroyed by us at a rate unprecedented in history.Call Number: [EL]ISBN/ISSN: 978-2-940529-99-5Physical Description: 83 p.

Anthropogenic biodiversity loss can impair ecosystem functioning. Human activities are often managed with the aim of reversing biodiversity loss and its associated functional impacts. However, it is currently unknown whether biodiversity–ecosystem function (BEF) relationships observed during biodiversity recovery are the same as those observed during biodiversity loss. This will depend on how species extirpation and recolonisation sequences compare and how different species influence ecosystem functioning. Using data from a marine benthic invertebrate community, we modelled how bioturbation potential – a proxy for benthic ecosystem functioning – changes along biodiversity loss and recovery sequences governed by species’ sensitivity to physical disturbance and recolonisation capability, respectively. BEF relationships for biodiversity loss and recovery were largely the same despite species extirpation and recolonisation sequences being different. This held true irrespective of whether populations were assumed to exhibit compensatory responses as species were removed or added. These findings suggest that the functional consequences of local biodiversity loss can be reversed by alleviating its drivers, as different species present at comparable levels of species richness during biodiversity loss and recovery phases have similar functional effects. Empirically verifying and determining the generality of our model-based results are potential next steps for future research. Methods Benthic invertebrate community data (population abundance and biomass of species) were collected using a 0.1 square metre Day grab from an area of offshore mud within the Fladen Ground, northern North Sea. Sixty stations were sampled over six survey boxes in April 2015. Species were assigned biological trait information that reflects their effects on ecosystem functioning (via bioturbation), their sensitivity to physical disturbance, and their capacity to recolonise post-disturbance using published databases (Queirós et al. 2013; Clare et al. 2022). These data were used to characterise an initial community and simulate the impact of biodiversity loss and recovery on benthic ecosystem functioning using a probabilistic model (sensu Solan et al. 2004, 2012; Thomsen et al. 2017; Garcia et al., 2021). Correlations between species sensitivity, and recolonisation capability, and population bioturbation potential were inspected to help interpret the results. Detailed descriptions of data collection, processing, and analysis are provided in the main article linked to the accompanying datasets. References: Clare DS, Bolam SG, McIlwaine PSO, Garcia C, Murray JM, Eggleton JE. 2022. Biological traits of marine benthic invertebrates in Northwest Europe. Scientific Data 9, 339. Garcia C, Solan M, Bolam SG, Sivyer D, Parker R, Godbold JA. 2021. Exploration of multiple post-extinction compensatory scenarios improves the likelihood of determining the most realistic ecosystem future. Environmental Research Communications 3, 045001. Queirós AM, Birchenough SNR, Bremner J, Godbold JA, Parker RE, Romero-Ramirez A, Reiss H, Solan M, Somerfield PJ, van Colen C, van Hoey G, Widdicombe S. 2013. A bioturbation classification of European marine infaunal invertebrates. Ecology and Evolution 3, 3958 – 3985. Solan M, Cardinale BJ, Downing AL, Engelhardt KA, Ruesink JL, Srivastava DS. 2004. Extinction and ecosystem function in the marine benthos. Science 306, 1177 – 1180. Solan M, Scott F, Dulvy NK, Godbold JA, Parker R. 2012. Incorporating extinction risk and realistic biodiversity futures: implementation of trait-based extinction scenarios. In Marine Biodiversity and Ecosystem Functioning: frameworks, methodologies, and integration, ed. Solan, M., Aspden RJ, Patterson DM, pp. 127 – 148. Oxford University Press, Oxford, UK. Thomsen MS, Garcia C, Bolam SG, Parker R, Godbold, JA, Solan M. 2017. Consequences of biodiversity loss diverge from expectation due to post-extinction compensatory responses. Scientific Reports 7, 43695.

Due to the dramatic biodiversity crisis, it is crucial to understand how people perceive biodiversity. Knowledge of how thoughts are organized around this concept can identify which ideas are best to focus on biodiversity conservation information campaigns. The primary aim of the present study was to identify social representations of the German public regarding the concept of biodiversity and its loss using a free word association test. Furthermore, unique association networks were analyzed. For this purpose, data collection was performed in September 2021 in Germany using an online questionnaire to assess participants’ associations with the prompt “biodiversity” (n  = 131) and “biodiversity loss” (n  = 130). Additionally, we used the social network software Gephi to create biodiversity (loss) association networks. The five most commonly mentioned associations for biodiversity were “animal,” “plant,” “nature,” “human,” and “flower.” For biodiversity loss, the five most commonly mentioned associations were “species extinction,” “climate change,” “plant,” “insect,” and “bee.” Neither “land use change” nor “invasive species,” as key drivers of biodiversity loss, were present in social representations of the German public. A difference was observed in the total number of mentioned associations between biodiversity and biodiversity loss. For both, the associations “plant” and “animal” were related. However, participants associated specific taxa only with animals, such as “insects” and “birds.” For plants, no specific taxa were named. Based on the network analysis, the most commonly mentioned word pairs for biodiversity and biodiversity loss were “plant – animal” and “species loss – climate change,” respectively. Based on our statistical network analysis, these associations were identified as the most central associations with the greatest influence in the network. Thus, they had the most connections and the function of predicting the flow in the network. In sum, the public’s multifaceted views on biodiversity and its loss, as well as the aforementioned central associations, hold great potential to be utilized more for the communication and education of biodiversity conservation. In addition, our findings contribute to the scientific community’s understanding of social representations and perceptions of biodiversity and its loss.

This geographic collection presents a cartographic comparison of three spatial analysis methodologies—Overlap, Multi K-means clustering, and Multi K-means applied to Variational Autoencoder (VAE) outputs—aimed at identifying the Risk of forest and grassland habitat degradation and the associated biodiversity loss.

The analysis integrates a set of environmental and socio-ecological variables considered critical for ecosystem risk evaluation, including:

• Land imperviousness density change

• Tree cover density

• Tree cover density change

• Grassland extent

• Grassland change

• Land use and cover Natura 2000 (classes: 1120 Industrial, commercial and military units; 1310 Mineral extraction, dump and construction sites; 8120 Highly modified watercourses and canals)

• Plant phenology index (total productivity)

• Temperature (average absolute change)

• Potential evapotranspiration (relative change)

• Precipitation (cumulative relative change)

• Number of species (cumulative)

• Land use and cover change

(Detailed references for the variables are provided in the links in the Related Works section.)

The comparison framework was implemented in a QGIS project, structured according to the WGS84 EPSG:4326 coordinate reference system. The project is organized into three main groups of layers:

1. Original variables – containing the spatial datasets of the raw variables.

2. Reclassified and inverted variables – including CSV datasets of variables reclassified and, when appropriate, inverted according to their ecological significance.

3. Risk of forest and grassland habitat degradation and biodiversity loss – comprising the outputs of the three methodologies, the corresponding heatmaps of risk hotspots, and the comparative assessments across methods.

This statistic displays the annual economic value of biodiversity lost due to food lost and wasted in North America in 2017, by country. As of 2017, *** million dollars worth of biodiversity was lost annually due to food lost and wasted in the United States.

The decline of biodiversity from anthropogenic landscape modification is among the most pressing conservation problems world-wide. In North America, long-term population declines have elevated the recovery of the grassland avifauna to among the highest conservation priorities. Because the vast majority of grasslands of the Great Plains are privately owned, the recovery of these ecosystems and bird populations within them depend on landscape-scale conservation strategies that integrate social, economic, and biodiversity objectives. The Conservation Reserve Program (CRP) is a voluntary program for private agricultural producers administered by the United States Department of Agriculture that provides financial incentives to take cropland out of production and restore perennial grassland. We investigated spatial patterns of grassland availability and restoration to inform landscape-scale conservation for a comprehensive community of grassland birds in the Great Plains. The research objectives were to 1) determine how apparent habitat loss has affected spatial patterns of grassland bird biodiversity, 2) evaluate the effectiveness of CRP for offsetting the biodiversity declines of grassland birds and 3) develop spatially explicit predictions to estimate the biodiversity benefit of adding CRP to landscapes impacted by habitat loss. We used the Integrated Monitoring in Bird Conservation Regions program to evaluate hypotheses for the effects of habitat loss and restoration on both the occupancy and species richness of grassland specialists within a continuum modelling framework. We found the odds of community occupancy declined by 37% for every 1 Standard Deviation (SD) decrease in grassland availability [loge(km2)] and increased by 20% for every 1 SD increase in CRP land cover [loge(km2)]. There was 17% turnover in species composition between intact grasslands and CRP landscapes, suggesting grasslands restored by CRP retained considerable, but incomplete representation of biodiversity in agricultural landscapes. Spatially explicit predictions indicated absolute conservation outcomes were greatest at high latitudes in regions with high biodiversity, whereas the relative outcomes were greater at low latitudes in highly modified landscapes. By evaluating community-wide responses to landscape modification and CRP restoration at bioregional scales, our study fills key information gaps for developing collaborative strategies, and balancing conservation of avian biodiversity and social well-being in agricultural production landscapes of the Great Plains.

Methods Sampling design

The study area corresponded to the Great Plains sampling frame from the Integrated Monitoring in Bird Conservation Regions (IMBCR) program (Pavlacky et al. 2017). The sampling frame was developed by superimposing a 1 km ×1 km grid over four BCRs in the study area, stratified by state and partner defined-regions, and 1-km2 sampling units were selected within each stratum using Generalized Random-tessellation Stratified (GRTS) sampling (Stevens and Olsen 2004). We sampled Bird Conservation Regions (BCR) 11, BCR 17 and BCR 18 in eastern Colorado every year from 2010 through 2018, but except for a small number of isolated strata, sampling in the greater BCR 18 and 19 began in 2016. We sampled the set of sampling units in successive years, but because annual sampling intensity within strata varied, some units were not sampled in successive years. We sampled 4,140 1-km2 sampling units within the study area from 2010 through 2018. The IMBCR design sampled vegetation types in proportion to availability within strata (Pavlacky et al. 2017), and we included all data in the analysis.

The sampling protocols for avian monitoring involved a two-stage design with systematic sub-samples of 16 point count plots located 250 m apart and ≥125 m from grid cell boundaries (Pavlacky et al. 2017). We monitored the occurrence of bird species at 44,849 point count plots on one visit per year from 2010 through 2018 using 6-min counts from one-half hour before sunrise to five hours after sunrise at each accessible point count location (Pavlacky et al. 2017). Field technicians measured distances to each bird detection using a laser rangefinder and we truncated distances < 125 m to specify 4.9-ha, non-overlapping point count plots (Pavlacky et al. 2012). We used a removal sampling protocol to estimate incomplete detection (MacKenzie et al. 2018), and binned the 6-min point count intervals into three, 2-min time occasions to maintain a constant detection rate in each occasion and ensure a monotonic decline in the detection frequency through time (Pavlacky et al. 2012).

Landscape covariates

We measured 3 continuous landscape composition covariates in 3 km × 3 km (9 km2) square landscape buffers surrounding the 1-km2 sampling units using remotely sensed data. We selected a 3 km × 3 km landscape buffer based on the eight 1 km2 grid cells neighboring the IMBCR sampling unit (Pavlacky et al. 2017) to allow a design-based hierarchal structure for predictions. The 9 km2 landscape buffer was similar in size to a grid of point counts buffered by the mean of the best-supported landscape radii for 6 grassland bird species (10 km2) studied by Niemuth et al. (2017). We quantified the area of grassland and shrubland vegetation in the 9-km2 landscapes using the LANDFIRE Existing Vegetation Type (EVT) spatial data layer (USGS 2016) using a Geographic Information System (GIS; ArcGIS Version 10.1, Environmental Systems Research Institute, Redlands, CA, USA), and the raster and spatialEco packages in the R statistical computing environment (R Version 3.5.2, www.r-project.org). We classified landscape composition as grassland or shrubland vegetation according to the EVT System Group Physiognomy field, except we reclassified three grassland types, two conifer-hardwood types and one hardwood type as shrubland based on review of the vegetation types. The grassland vegetation was composed of native grassland vegetation, as well as agricultural grasslands such as pastures and hay fields. In addition, we measured the area of CRP in the 9-km2 landscapes using Common Land Unit spatial data (USDA 2014). We included only the CRP conservation practices that involved grassland or wetland cover types, and removed practices involving tree cover and parcels containing missing practice information across all years. For missing practice information within a particular year, including all CRP raster data from 2008 - 2010, we updated values with data from the closest available year, with the exception of CRP parcels with an expiration date > 15 years after the data year or parcels with a missing expiration date. When possible, we replaced missing parcel data at the county or state level with data from the closest available year. We intersected the annual CRP land-cover data and replaced the intersected land cover with CRP to arrive at seamless annual vegetation mosaics composed of grassland, shrubland and CRP land cover. In addition to the landscape composition covariates, we used GIS to calculate latitude and longitude for the centroid of the 1-km2 sampling units.

Statistical analysis

We loge transformed the land cover covariates [loge(1 + km2)] to allow non-linear and threshold responses to landscape features, and centered and standardized all covariates using the z-transformation (Schielzeth 2010).

Literature Cited

Pavlacky D. C. Jr., J. A. Blakesley, G. C. White, D. J. Hanni, and P. M. Lukacs. 2012. Hierarchical multi-scale occupancy estimation for monitoring wildlife populations. Journal of Wildlife Management 76:154-162.

Pavlacky, D. C., Jr., P. M. Lukacs, J. A. Blakesley, R. C. Skorkowsky, D. S. Klute, B. A. Hahn, V. J. Dreitz, T. L. George, and D. J. Hanni. 2017. A statistically rigorous sampling design to integrate avian monitoring and management within Bird Conservation Regions. PLOS ONE 12:e0185924.

Stevens, D. L., Jr., and A. R. Olsen. 2004. Spatially balanced sampling of natural resources. Journal of the American Statistical Association 99:262-278.

MacKenzie, D. I., J. D. Nichols, J. A. Royle, K. H. Pollock, L. L. Bailey, and J. E. Hines. 2018. Occupancy estimation and modeling: inferring patterns and dynamics of species occurrence. Second edition. Academic Press, London, UK.

Niemuth, N. D., M. E. Estey, S. P. Fields, B. Wangler, A. A. Bishop, P. J. Moore, R. C. Grosse, and A. J. Ryba. 2017. Developing spatial models to guide conservation of grassland birds in the U.S. Northern Great Plains. The Condor: Ornithological Applications 119:506-525.

Schielzeth, H. 2010. Simple means to improve the interpretability of regression coefficients. Methods in Ecology and Evolution 1:103-113.

United States Department of Agriculture [USDA]. 2014. Common Land Unit geospatial data. Memorandum of understanding between the USDA and Bird Conservancy of the Rockies, signed 4 August 2014. USDA, Farm Service Agency, Economic and Policy Analysis, and Commodity Credit Corporation, Washington, D. C., USA.

United States Geological Survey [USGS]. 2016. Landfire 1.4.0: existing vegetation cover layer. United States Department of the Interior, Geological Survey, Sioux Falls, South Dakota, USA. http://landfire.cr.usgs.gov/viewer. Accessed 23 January 2018.

Protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss : Most countries in the region retain sizeable sections of forested area, although habitat loss continues to be a risk due to unsustainable logging practices and forest conversion for plantation and agricultural uses. Invasive species are the leading cause of species extinction on island ecosystems and can impact food and economic security. A regionally co-ordinated response is in place for the management of invasive species in the Pacific; Ecosystem-based approaches are being integrated into national and sector plans, with potential benefits including enhanced livelihood opportunities and food security; increased biodiversity conservation; and improved carbon sequestration and sustainable water management. The Red List Index, is an indicator of changes in biodiversity and species extinction risk over time.

Find more Pacific data on PDH.stat.

Annex 1 contains a list of organizations whose websites were scanned for relevant litigation cases. Annex 2 provides the full list of all litigation cases that were found. The cases are ordered by strategy. The Annex includes a reference to the case documents, a short description of all the cases that were found, as well as the case name, country, and the issue that the litigation case focuses on.

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).

Census PointsPoint-level covariatesCensus_Points.csvBird survey dataCollected and identified by Jacob B. Socolar. Please consider offering co-authorship on any publication that makes extensive use of these data.birds.csvRiver island birds (Rosenberg)Commonness of bird species on river islands, based on Rosenberg (1990)RosenbergCU.csvCensus point data with remote sensingEquivalent to Census_Points.csv, but stored as a spatialpointsdataframe, and includes primary forest cover covariates extracted from Landsat imagery.points_sp_data.rdataTree survey dataData from tree surveys near Iquitos, Peru (one row per stem)trees.csvR script for analysisPerforms the analyses described in Socolar et al 2019 (Conservation Biology)RCode.RReadMEDescriptions of data files

The dilution effect predicts increasing biodiversity to reduce the risk of infection, but the generality of this effect remains unresolved. Because biodiversity loss generates predictable changes in host community competence, we hypothesized that biodiversity loss might drive the dilution effect. In this file, we test this hypothesis by reanalyzing four previously published meta-analyses that came to contradictory conclusions regarding generality of the dilution effect.

AbstractClimate change and biodiversity loss are expected to simultaneously affect ecosystems, however research on how each driver mediates the effect of the other has been limited in scope. The multiple stressor framework emphasizes non-additive effects, but biodiversity may also buffer the effects of climate change, and climate change may alter which mechanisms underlie biodiversity-function relationships. Here, we performed an experiment using tank bromeliad ecosystems to test the various ways that rainfall changes and litter diversity may jointly determine ecological processes. Litter diversity and rainfall changes interactively affected multiple functions, but how depended on the process measured. High litter diversity buffered the effects of altered rainfall on detritivore communities, evidence of insurance against impacts of climate change. Altered rainfall affected the mechanisms by which litter diversity influenced decomposition, reducing the importance of complementary attributes of species (“complementarity effects”), and resulting in an increasing dependence on the maintenance of specific species (“dominance effects”). Finally, altered rainfall conditions prevented litter diversity from fuelling methanogenesis, because such changes in rainfall reduced microbial activity by 58%. Together, these results demonstrate that the effects of climate change and biodiversity loss on ecosystems cannot be understood in isolation and interactions between these stressors can be multifaceted. Usage notestank bromeliad experiment (Pires et al)This file contains the data used to produce the manuscript "Interactive effects of climate change and biodiversity loss on ecosystem functioning" by Pires et al in Ecology. The first sheet contains data of decomposition (dry weight loss in mg after 180 days), bacterial production (µmol C L-1 h-1), detritivore abundance (number of detritivore individuals in each tank bromeliad), detritivore richness (number of detritivore species in each tank bromeliad), bromeliad maximum volume (ml) and the mechanisms by which litter diversity affected decomposition (TICE: trait-independent complementarity effect; DE: dominance effect and TDCE: trait-dependent complementarity effect). The second sheet contains data of methane concentration (ppm) in three different sampling times (30, 60 and 90 days after rainfall manipulation). The first three columns in each sheet describe the treatments used in the experiment. For more details about the experimental design, sampling and methods used to produce these data, see the manuscript in the journal.

Many experiments have shown that species loss can alter key processes important to the productivity and sustainability of Earth's ecosystems. However, it is unclear how these effects compare to the direct effects of other forms of environmental change that are both driving diversity loss and altering ecosystem function. We used a suite of meta-analyses of published data to show that the impacts of species loss on productivity and decomposition - two processes important in all ecosystems are of comparable magnitude to impacts of many other global environmental changes. These analyses were published by Hooper et al. in Nature in 2012 (online release date: May 2, 2012, DOI: 10.1038/nature11118). This archive contains the data and statistical details for the two sets of analyses found in that paper. In the first, we performed a broad meta-analysis to compare effects of changing species richness on primary production and decomposition, as derived from a database on biodiversity-ecosystem functioning (BEF) experiments, with effects of major environmental changes as derived from already published meta-analyses. For both biodiversity and environmental effects, we use log response ratios for effect sizes. This analysis allows a broad comparison across many experiments, but in so doing, compares effects of species richness with those of other environmental changes in different experiments performed by different researchers in different ecosystems. Therefore, we undertook a second analysis focusing on the relative magnitude of effects of species richness and environmental change in experiments that factorially manipulated both. For this, we used a much smaller subset of experiments from the BEF database, and only analyzed primary production as the response variable. Here we include the data and any processing steps, including R-code and description, that were used in our analyses.

In experiments, intermediate levels of species loss (21-40%) reduced plant production by 5-10%, comparable to previously documented impacts of ultra-violet radiation and climate warming. Higher levels of extinction (41-60%) had impacts rivaling those of ozone, acidification, elevated CO2, and nutrient pollution. At intermediate levels, species loss generally had equal or greater impacts on decomposition than did elevated CO2 and nitrogen addition. The identity of species lost also had a large impact on changes in productivity and decomposition, generating a wide range of plausible outcomes for extinction. Despite need for more studies on interactive effects of diversity loss and environmental changes, our analyses clearly show that the ecosystem consequences of local species loss are as quantitatively significant as direct effects of several global change stressors that have mobilized major international concern and remediation efforts.

Understanding the impact of ecological factors on biodiversity is central in the context of accelerating climate change and biodiversity loss. Urban areas, as landscapes under particularly strong anthropogenic influence, are undergoing rapid ecological change, yet the consequences for urban biodiversity and ecosystem functioning remain poorly understood. In this study, we focused on fruit flies of the genus Drosophila - a diverse group of dipterans with variable ecological niches and degrees of synanthropy - to investigate species composition and community ecology in the metropolitan area of Vienna, Austria. With the help of numerous citizen scientists, we have collected approximately 18,000 specimens through dense spatio-temporal sampling both indoors and outdoors of human dwellings. A total of 13 Drosophila species were identified, with communities dominated by widespread cosmopolitan synanthropic species. Among these, D. mercatorum and D. virilis represent novel records for Austria. Comparisons to a previous study from more than 30 years ago revealed that the species richness in Vienna was more than 50% lower than before and showed that formerly common species were potentially replaced by neobiots. We further assessed ecological niches by intersecting species abundance data with high-dimensional, high-resolution earth observation datasets, which revealed distinct ecological preferences among species. In particular, the neozoan D. mercatorum emerged as a highly synanthropic species, tightly confined to urban areas with high levels of imperviousness. In summary, our study underpins the versatility of the Drosophila system as indicators of biodiversity loss in a rapidly changing world.

This dataset contains the raw abundance data of the observed species, as well as point data and gridded datasets of eorth observartion data used for ecological inference.

The Millennium Ecosystem Assessment: MA Biodiversity provides data and information on amphibians, disease agents (extent and distribution of infectious and parasitic diseases), drylands (cattle, sheep and goats, and pasture), islands (fishing pressure, sewage pollution index and tourism), loss of natural land cover (biomes and realms), polar population, species distribution models, and terrestrial ecoregions and realms. Biodiversity is defined as the variability among living organisms from all sources, including terrestrial, marine, and other aquatic ecosystems and the ecological complexes of which they are a part. The original information was received from multiple sources that include the International Union for Conservation of Nature (IUCN, formerly the World Conservation Union), the World Wildlife Fund (WWF), the History Database of the Global Environment (HYDE) of Netherlands Environmental Assessment Agency (PBL), and the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on-board NASA satellites Terra and Aqua. Through the Convention on Biological Diversity, United Nations Convention to Combat Desertification, Ramsar Convention on Wetlands, and the Convention on Migratory Species, the data were also designed to meet the needs of stakeholders in the business, civil and native commUnities.

The statistic shows the seriousness of issues related to the decline and loss of biodiversity in Italy in 2015. According to the survey, the majority of respondents (** percent) believed that the decline and loss of natural habits such as forests, fields, and swamps is a very or quite alarming issue.