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Supplementary material 1 from: Dainese M, Poldini L (2012) Does residence time affect responses of alien species richness to environmental and spatial processes? NeoBiota 14: 47-66. https://doi.org/10.3897/neobiota.14.3273
Supplementary material 1 from: Dainese M, Poldini L (2012) Does residence time affect responses of alien species richness to environmental and spatial processes? NeoBiota 14: 47-66. https://doi.org/10.3897/neobiota.14.3273
Data from: Species richness and evenness of European bird communities show differentiated responses to measures of productivity
<p>Understanding patterns of species diversity is crucial for ecological research and conservation, and this understanding may be improved by studying patterns in the two components of species diversity, species richness and evenness of abundance of species. Variation in species richness and evenness has previously been linked to variation in total abundance of communities as well as productivity gradients. Exploring both components of species diversity is essential because these components could be unrelated or driven by different mechanisms. The aim of this study was to investigate the relationship between species richness and evenness in European bird communities along an extensive latitudinal gradient. We examined their relationships with latitude and Net Primary Productivity, which determines energy and matter availability for heterotrophs, as well as their responses to territory densities (i.e., the number of territories per area) and community biomass (i.e., the bird biomass per area). We applied a multivariate Poisson log-normal distribution to unique long-term, high-quality time-series data, allowing us to estimate species richness of the community as well as the variance of this distribution, which acts as an inverse measure of evenness. Evenness in the distribution of abundance of species in the community was independent of species richness. Species richness increased with increasing community biomass, as well as with increasing density. Since both measures of abundance were explained by NPP, species richness was partially explained by energy-diversity theory (i.e., the more energy, the more species sustained by the ecosystem). However, species richness did not increase linearly with NPP but rather showed a unimodal relationship. Evenness was not explained either by productivity nor by any of the aspects of community abundance. This study highlights the importance of considering both richness and evenness to gain a better understanding of variation in species diversity. We encourage the study of both components of species diversity in future studies, as well as use of simulation studies to verify observed patterns between richness and evenness.</p>
Fig. 5 in Species Accumulation Curves And Similarity Traits Of A Species-Rich Fly (Diptera) Community
Fig. 5. Jackknifed NESS indices relating to the kth and (k + 1)th group of 50, k = 1,2,…, 19. In case of k = 8 and k = 14 samples from different years are compared
Fig. 2. Sample-based curve for 2003–2005 in Species Accumulation Curves And Similarity Traits Of A Species-Rich Fly (Diptera) Community
Fig. 2. Sample-based curve for 2003–2005. Two points, corresponding to species numbers after adding the first group of ten to the sample in 2004 and 2005 are denoted by empty symbol
Supplementary data from: Current and past climate co-shape community-level plant species richness in the Western Siberian Arctic
<p>The Arctic ecosystems and their species are exposed to amplified climate warming and, in some regions, to rapidly developing economic activities. We used macroecological modeling to estimate the community-level species richness across the Western Siberian tundra, with climate variables and anthropogenic influence identified as main explanatory factors. Our results reveal complex spatial patterns of community-level species richness in the Western Siberian Arctic. We show that climatic factors such as temperature (including paleotemperature) and precipitation are the main drivers of plant species richness in this area, and the role of relief is clearly secondary.</p> <p>Here we present a supplementing dataset to the analysis of our paper "Current and past climate co-shape community-level plant species richness in the Western Siberian Arctic"<strong> </strong>(<a href="https://doi.org/10.1002/ece3.11140">https://doi.org/10.1002/ece3.11140</a>). Our research is based on the Western Siberian part of the Russian Arctic Vegetation Archive (AVA-RUS, <a href="http://avarus.space">http://avarus.space</a>), with 1483 Braun-Blanquet plots observed from 2005-2018.</p> <p>The dataset contains geolocated species richness data along with sampled raster data on environmental and anthropogenic predictors used for modeling. The scripts are used for paleoclimatic data sampling; testing univariate predictive performance and limited collinearity for all predictors; fitting four different modes: random forest, gradient boosting machine, generalized linear model, and generalized additive model; their validation and projection. Detailed information regarding the data structure and the applied methods could be found in the paper.</p>
Figure 2. – Monthly average species richness observed during diurnal counts from June 2013 in Assessing structure and seasonal variations of a temperate shallow water fish assemblage through Snorkel Visual Census
Figure 2. – Monthly average species richness observed during diurnal counts from June 2013 to August 2014. Error bars represent ±SD. Number of counts per month are, indicated at column bases.
FIGURE 10 in Palaeoecology and sea level changes: Decline of mammal species richness during late Quaternary island formation in the Montebello Islands, north-western Australia
FIGURE 10. Number of species identified from each spit in Morgan's Cave, illustrating the increasing loss of species from spits four to one.
FIGURE 9 in Palaeoecology and sea level changes: Decline of mammal species richness during late Quaternary island formation in the Montebello Islands, north-western Australia
FIGURE 9. Species-area plot for islands on the north-west continental shelf (filled circles) (data from Abbott and Burbidge, 1995), and spits one to seven in Morgan's Cave (open circles).
Figure 1 in Species richness of urban and rural fish assemblages in the Grijalva Basin floodplain, southern Gulf of Mexico
Figure 1. – Map of the study area in the floodplain of the Grijalva Basin. Sampling sites (ID), 1 = Gordiano, 2 = La Pólvora, 3 = Ilusiones, 4 = Costeñito and 5 = Loma de Caballo urban lagoons (circles); 6 = Campo, 7 = Manguito, 8 = Pucté, 9 = Maluco and 10 = Playa de Poza rural lagoons (stars); and 11 = González, 12 = Carrizal, 13 = Grijalva, 14 = Pichucalco and 15 = Samaria rivers (triangles) in rural sites. MR = Mezcalapa River; MvR = Mezcalapa Viejo River. Map modified of http:// antares.inegi.org.mx/analisis/red_ hidro/SIATL/#
FIGURE 8 in Palaeoecology and sea level changes: Decline of mammal species richness during late Quaternary island formation in the Montebello Islands, north-western Australia
FIGURE 8. Log non-volant species vs log area plot for the north-west islands (filled circles) and the super-island at sea level 10 m below present (open circle).
FIGURE 6 in Palaeoecology and sea level changes: Decline of mammal species richness during late Quaternary island formation in the Montebello Islands, north-western Australia
FIGURE 6. Species accumulation curve for Morgan's Cave, showing increasing species with increased sampling effort (cumulative NISP). Further sampling effort could have yielded more species.
Fig. 3 Canonical correspondence analysis. Only axes 1 and 2 are shown. Type 2 in Evaluating the correlation between area, environmental heterogeneity, and species richness using terrestrial isopods (Oniscidea) from the Pontine Islands (West Mediterranean)
Fig. 3 Canonical correspondence analysis. Only axes 1 and 2 are shown. Type 2 scaling is shown. A right-angled projection of a point representing a response variable (ecological categories of species) onto an arrow representing an explanatory variable (biotope type)
Fig. 2 in Evaluating the correlation between area, environmental heterogeneity, and species richness using terrestrial isopods (Oniscidea) from the Pontine Islands (West Mediterranean)
Fig. 2 Path analysis model. In this model, species richness (S) is the dependent variable. Area (A) and environmental heterogeneity (H) can have a direct effect on S, whereas A can also have an effect on H. The indicators used for A and S are the log-transformed area in square kilometres (LogA) and the number of species (LogS). Different indicators were used for environmental heterogeneity (B, LogB, Shannon, and 1-D, see main text). The symbols bAS, bAH, and bHS indicate the partial standardised regression coefficients
Data for "Sounding out Ecoacoustic Metrics: Avian species richness is predicted by acoustic indices in temperate but not tropical habitats"
<p>This deposit contains the data for the paper <strong>A Multi-habitat, Comparative Evaluation of Ecoacoustic Indices for Biodiversity Monitoring: Acoustic Indices Predict Avian Species Richness in Temperate but not Tropical Habitats. (Ecological Indicators) </strong>The dataset contains a series of 1 min wav files recorded across UK and Ecuadorian habitats. Each one has 26 acoustic indices calculated on it, and a full list of avian species and abundances and GPS data for each sample site.</p> <p>Abstract</p> <p>Affordable, autonomous recording devices facilitate large scale acoustic monitoring and Rapid Acoustic Survey is emerging as a cost-effective approach to ecological monitoring; the success of the approach rests on the development of computational methods by which biodiversity metrics can be automatically derived from remotely collected audio data. Dozens of indices have been proposed to date, but systematic validation against classical, in situ diversity measures. This study conducted the most comprehensive comparative evaluation to date of the relationship between avian species diversity and a suite of acoustic indices across a wide range of ecological conditions. Acoustic surveys were carried out across habitat gradients in temperate and tropical biomes. Baseline avian species richness and subjective multi-taxa biophonic density estimates were established through aural counting by expert ornithologists. 26 acoustic indices were calculated and compared to observed variations in species diversity. Five acoustic diversity indices (Bioacoustic Index, Acoustic Diversity Index, Acoustic Evenness Index, Acoustic Entropy, and the Normalised Difference Sound Index) were assessed as well as three simple acoustic descriptors (root-mean-square, spectral centroid and zero-crossing rate). Highly significant correlations, of up to 65%, between acoustic indices and avian species richness were observed across temperate habitats, supporting the use of automated acoustic indices in biodiversity monitoring where a single vocal taxon dominates. Significant, weaker correlations were observed in neotropical habitats which host multiple non-avian vocalizing species. Multivariate classification analyses suggest that AIs also track observed differences in habitat-dependent community composition and that each habitat has a distinct soundscape. Multivariate analyses of the relative predictive power of AIs show that compound indices are more powerful predictors of avian species richness than any single index and simple descriptors contribute to predicting avian diversity in multi-taxa tropical environments. Our results support the use of community level acoustic indices as a proxy for species richness and point to the potential for tracking of habitat-dependent changes in community composition. Recommendations for the design of compound indices for multi-taxa community composition appraisal are put forward, with consideration for the requirements of next generation, low power remote monitoring networks.</p> <p> </p> <p><strong>Sampling Methods (extract from paper)</strong></p> <p>Acoustic surveys were carried out along a gradient of habitat degradation (1 forested, 2 regenerating forest and 3 agricultural land) in South East (SE) England and North Western (NW) Ecuador. The six sites (UK1, UK2, UK3, EC1, EC2, EC3) were sampled consecutively from May 6th - Aug 25th 2015.</p> <p>All UK sites were in the county of Sussex, in SE England, an area of weald clays (Fig. 2, left) and included ancient woodland (UK1), regenerating farmland with patches of woodland (UK2) and a downland barley farm (UK3).1 min mono audio recordings made every 15 minutes at three different habitats in the UK</p> <p>Ten day acoustic surveys were carried out consecutively at each study site using 15 Wildlife Acoustics Song Meter audio field recorders. Sampling points were arranged in a grid at a minimum distance of 200 m to minimise pseudo replication (the sound of most species being attenuated over this distance in all biomes). Altitudinal range of sample points across sites was minimised in order to prevent introduction of extraneous, confounding gradients (UK varied between 10 m – 50 m and Ecuador 130 m – 390 m). Recording schedules captured 1 min every 15 min around the clock for 10 days at each site, resulting in 960 recordings at each of 15 sample points for 3 habitat types in 2 different climates (86,400 1 minute recordings in total). Data across the 15 sample points was pooled; inter-site variation was not explored in the current analyses. In the UK 3½ hours of each dawn chorus was sampled starting at 1 hour before sunrise. This range was determined to capture the onset, progression and peak of the dawn chorus, creating a temporal gradient. The equatorial dawn chorus is more compact and was sampled for 2¼ hours starting 15 mins before sunrise, capturing a comparable chorus onset and peak.</p> <p> </p> <p> </p>
The Optimal Species Richness Environments for Human Populations
<p>This supporting document should allow one to recreate the analysis performed as part of “The optimal species richness environments for human populations, ” By Freeman et al. 2018 Submitted to PNAS July 2018. The annotated scripts in this directory (RichnessSIScripts.pdf) contain code to replicate the analysis, as well as code for additional analyses not included in the main paper or supplemental information. To replicate the analysis, one can either analyze the data files provided or build their own data set. As discussed in the main body of the text, we built three data sets following the procedures outlined by Tallavaara et al. (2017) for linking species richness values, net primary productivity and pathogen stress to each ethnographic case. We do not replicate the scripts provided by Tallavaara et al.(2017) as these are available, clear and should be cited when used.To replicate our analysis, one needs to set their working directory in R to the file location that contains the data files. There are 11 files that follow the naming convention “name.csv.” The 11 files are “MainFinal.csv”. “AGPOP3Eco.csv”, “HGFEM4R.csv”, “AGPOPClass.csv”, “CountryMeansEco2.csv”, “AGPOP3EcoH.csv”, “AGPOP3EcoL.csv”, “HiHG.csv”, “LowHG,csv”, “CountryMeansEco2H.csv”, and “CountryMeansEco2L.csv”. The first five files are the main files, the second six files are divided into high and low species richness environments by economy type for convenience. In each file, the variables are defined as follows:</p> <p>1. Group/Country–name of the ethnographic society of country</p> <p>2. Latitude–the latitude at the geographic center of a group’s territory or a country’s territory.</p> <p>3. Longitude–the longitude at the geographic center of a group’s territory or a country’s territory.</p> <p>4. Class–an ordinal ranking of wealth and status differentiation among the hunter-gatherer and agriculturalists societies (see main text for more details)</p> <p>5. Class2–an binary ranking of wealth and status differentiation among the hunter-gatherer and agriculturalists societies (see main text for more details).</p> <p>6. ECI–The average economic complexity index since 1973 as measured among modern countries.</p> <p>7. DENSITY–Population density in people per square kilometer. This is a point in time estimatefor hunter-gatherer and agricultural groups and an average density since 1973 among nation states.</p> <p>8. LnDENSITY–The natural log of population density</p> <p>9. npp–net primary productivity estimated at the center of each group’s territory</p> <p>10. npp2-Net primary productivity squared</p> <p>11. biodiv–Standardized estimate of species richness at the center of each group’s range.</p> <p>12. biodiv2–Species richness *100 ad squared.</p> <p>13. pathos–Index of pathogen stress at the center of a group’s territory.</p> <p>14. DivDiff–The absolute value of species richness-the species richness value of peak population density (values identified in Fig. 2 of the main manuscript).1</p> <p>5. ID–A nominal variable that denotes economy type. HG=hunter-gatherer, AG=subsistence agriculturalist, IND=modern nation state</p> <p> </p> <p>Tallavaara, M., J. T. Eronen, and M. Luoto2017. Supporting data and script for ”productivity, biodiversity, and pathogens influence the global hunter-gatherer population density” (Tallavaara et al. pnas 2018).<a href="https://doi.org/10.5281/zenodo.1167852">https://doi.org/10.5281/zenodo.1167852</a></p>
Fig 9. Top 10 in Publishing trends and productivity in insect taxonomy from 1946 through 2012 based on an analysis of the Zoological Record for four species-rich families
Fig 9. Top 10 of most utilized journals from 1946 to 2012. A. Cicadellidae. B. Miridae. C. Pyralidae. D. Staphylinidae.
Fig 6 in Publishing trends and productivity in insect taxonomy from 1946 through 2012 based on an analysis of the Zoological Record for four species-rich families
Fig 6 (continued from previous page). Number of total articles and co-authored articles from 1946 to 2012. A. Cicadellidae. B. Miridae. C. Pyralidae. D. Staphylinidae.
Fig 6 in Publishing trends and productivity in insect taxonomy from 1946 through 2012 based on an analysis of the Zoological Record for four species-rich families
Fig 6 (continued on next page). Number of total articles and co-authored articles from 1946 to 2012. A. Cicadellidae. B. Miridae. C. Pyralidae. D. Staphylinidae.
Fig 5 in Publishing trends and productivity in insect taxonomy from 1946 through 2012 based on an analysis of the Zoological Record for four species-rich families
Fig 5 (continued on next page). Number of articles published by continent (Europe, North America, South America, Africa, Asia and Australia) between 1946 and 2012. A. Cicadellidae. B. Miridae. C. Pyralidae. D. Staphylinidae.
Fig 2 in Publishing trends and productivity in insect taxonomy from 1946 through 2012 based on an analysis of the Zoological Record for four species-rich families
Fig 2. Average new species described per article (Cicadellidae, Miridae, Pyralidae and Staphylinidae combined) from 1946 to 2012.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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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.