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16 results for “diversity curves”
Data from: Flattening the curve: approaching complete sampling for diverse beetle communities
<p><strong>DATA FROM:</strong></p> <p>Burner, R., J. Åstrom, T. Birkemoe, A. Sverdrup-Thygeson. 2021. Flattening the curve: approaching complete sampling for diverse beetle communities. <em>Insect Conservation and Diversity</em> <a href="https://doi.org/10.1111/icad.12540">https://doi.org/10.1111/icad.12540</a> </p> <p> </p> <p><strong>ACKNOWLEDGEMENTS</strong></p> <p>This research was funded by the Norwegian Environment Directorate as part of an ‘Agreement on monitoring hollow oaks and insects in hollow oaks’. The Norwegian University of Life Sciences (NMBU) workshop designed and produced the cross-pane flight intercept traps. Thanks to Sindre Ligaard for identifying the beetle species, and to Lindsay Burner, Ruben Roos, and Ross Wetherbee for assistance in the field. High-performance computing resources were provided by Frederick H. Sheldon and Louisiana State University (LSU HPC).</p> <p><strong>INFORMATION</strong></p> <p>This dataset contains all data necessary to reproduce the analysis in the resulting manuscript. Briefly, 110 insect traps were set for 3 months in a single forest stand in Ås, Norway in 2020. This dataset includes trap locations, number of individuals of each species captured in each trap, trap type, and forest covariates collected around the traps.</p> <p>For more detailed information see manuscript and README file.</p> <p>From abstract of manuscript:</p> <ol> <li>Insects are a hyper diverse and ecologically important group. Their high diversity, however, presents challenges in sampling methodology, because rare species are unreliably detected with low sampling effort. However, the relationship between effort and species detections, critical for effective monitoring and evaluation of population trends, is too seldom quantified.</li> <li>We sampled forest beetles for three months in a 4-ha stand of mixed deciduous forest in southeastern Norway using 110 flight intercept (four types) and Malaise traps, the highest trap density (29 traps/ha) that we have seen reported. We examined species accumulation curves to quantify the benefits of each additional trap, compared capture rates among several trap designs and trap emptying frequencies, and tested for spatial autocorrelation.</li> <li>In total we captured 566 beetle taxa (19,854 individuals) from 52 families, yet our species accumulation curve was only beginning to flatten. Trap types differed considerably in their effectiveness. Nevertheless, twenty of our most effective window traps detected 75% of all taxa in our dataset. We found no evidence of spatial correlation within the scale of the study (100 m radius), nor did trap-level forest covariates (5 m radius) explain much variation.</li> <li>This implies that low to moderate sampling effort dramatically underestimates species richness, but that a limited number of effective traps can nonetheless achieve relatively thorough sampling for some applications. Immediate trap surroundings and spacing appeared unimportant. But, insect ecologists should take particular care in selecting trap types and be cautious comparing studies that employed different trap types.</li> </ol> <p> </p>
Figure 2. Shannon–Wiener curve. X in Microbial diversity of ticks and a novel typhus group Rickettsia species (Rickettsiales bacterium Ac37b) in Inner Mongolia, China
Figure 2. Shannon–Wiener curve. X-axis: amount of sequencing data; Y-axis: corresponding Shannon diversity index.
Fig. 1. Sample completeness curve for a in Abundance and diversity of beneficial and pest arthropods in buckwheat on blueberry and vegetable farms in north Florida
Fig. 1. Sample completeness curve for a vegetable farm (dark shaded area, 38 samples) and blueberry farm (light shaded area, 19 samples) in Suwannee County, Florida. The solid line is the interpolated insect family-level richness and the dashed line is the extrapolated richness. The shaded areas represent 95% confidence intervals. Sampling effort is standardized based on the sample completeness (the proportion of sampled families to predicted families) at each sampling level, with the circle representing final sampling completeness of 38 samples taken at the vegetable farm and the triangle representing final sampling completeness of 19 samples taken at the blueberry farm.
Fig. 4. Rarefaction curves with extrapolations and 95 in Diversity and microhabitat use of benthic invertebrates in an urban forest stream (Southeastern Brazil)
Fig. 4. Rarefaction curves with extrapolations and 95% of confidence intervals for both the total sampling and each type of microhabitat of benthic invertebrates of Tijuca River, located at the Tijuca Forest, Rio de Janeiro, Brazil.
Data from: Accumulation curves of environmental DNA sequences predict coastal fish diversity in the Coral Triangle
Environmental DNA (eDNA) has the potential to provide more comprehensive biodiversity assessments particularly for vertebrates in species-rich regions. Yet, this method requires the completeness of a reference database, i.e. a list of DNA sequences attached to each species, which is never met. As an alternative, a diversity of Operational Taxonomic Units (OTUs) can be extracted from eDNA metabarcoding. However, the extent to which the diversity of OTUs provided by a limited eDNA sampling effort can predict regional species diversity is unknown. Here, by modelling OTU accumulation curves of eDNA seawater samples across the Coral Triangle, we obtained an asymptote reaching 1,531 fish OTUs while 1,611 fish species are recorded in the region. Besides, we also accurately predict (R² = 0.92) the distribution of species richness among fish families from OTU-based asymptotes. Thus, the multi-model framework of OTU accumulation curves extends the use of eDNA metabarcoding in ecology, biogeography and conservation.
Data from: Accumulation curves of environmental DNA sequences predict coastal fish diversity in the Coral Triangle
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Photosynthetic light-response curves:FAB 1 : Forests and Biodiversity Experiment - High density diversity
A forest biodiversity experiment (FAB) focused on trees of our region investigates the consequences of multiple dimensions of tree diversity for soil, food webs, plant communities and ecosystems. FAB is designed to unravel effects of three forms of biological diversity: species richness (SR), functional diversity (FD), and phylogenetic diversity (PD). We define FD as the representation of multiple traits of leaves, roots, seeds, and the whole organism that are correlated with species positions along gradients of resource supply, growth, and decomposition. PD is the representation of evolutionary lineages measured as the genetic distances between species. While PD and FD are often correlated, convergent evolution and adaptive differentiation can decouple them. When functional traits that drive specific ecosystem functions are not phylogenetically conserved, PD and FD may give contrasting predictions. SR, PD, and FD are not independent, and we posit that PD may help explain SR effects, and FD may help explain both PD and SR effects. Thus FAB is designed to examine the separate and combined effects of all three components of diversity for multiple ecosystem functions and to distinguish between ???sampling??? and ???complementarity??? effects of biodiversity. Due to the long lag between planting tree seedlings and determining effects of tree composition and diversity on ecosystem functioning, fewer experiments have been established to elucidate the role of biodiversity in the functioning of forest ecosystems than grassland experiments. FAB will contribute to this gap and is a member of the IDENT and TreeDiv network of forest biodiversity experiments (www.treedivnet.ugent.be). Hypotheses: 1. PD, FD, and SR will all contribute to increased productivity, stability, and diversity of other trophic levels (herbivores, predators, parasitoids, soil microbes, soil flora and fauna) as well as to greater soil C sequestration. 2. Because PD incorporates both the number of species a
FIG. 2. Sample-based species accumulation curve with 95 in An African bat hotspot: the exceptional importance of Mount Nimba for bat diversity
FIG. 2. Sample-based species accumulation curve with 95% confidence intervals for bat species at Mount Nimba
Data from: Grow where you thrive, or where only you can survive? An analysis of performance curve evolution in a clade with diverse habitat affinities
Performance curves are valuable tools for quantifying the fundamental niches of organisms and testing hypotheses about evolution, life history trade-offs, and the drivers of variation in species' distribution patterns. Here, we present a novel Bayesian method for characterizing performance curves that facilitates comparisons among species. We then use this model to quantify and compare the hydrological performance curves of 14 different taxa in the genus Lasthenia, an ecologically diverse clade of plants that collectively occupy a variety of habitats with unique hydrological features, including seasonally flooded wetlands called vernal pools. We conducted a growth chamber experiment to measure each taxon's fitness across five hydrological treatments that ranged from severe drought to extended flooding, and identified differences in hydrological performance curves that explain their associations with vernal pool and terrestrial habitats. Our analysis revealed that the distribution of vernal pool taxa in the field do not reflect their optimal hydrological environments: all taxa, regardless of habitat affinity, have highest fitness under similar hydrological conditions of saturated soil without submergence. We also found that a taxon's relative position across flood gradients within vernal pools is best predicted by the height of its performance curve. These results demonstrate the utility of our approach for generating insights into when and how performance curves evolve among taxa as they diversify into distinct environments. To facilitate its use, the modeling framework has been developed into an R package (https://github.com/silastittes/performr).
Fig. 4 Species accumulation curves. a in Assessment of hidden diversity of crinoids and their symbionts in the Bay of Nhatrang, Vietnam
Fig. 4 Species accumulation curves. a Accumulation of crinoid species as a function of collecting effort (number of crinoid specimens collected). b Accumulation of symbiont species as a function of collecting effort (number of crinoid specimens collected)
Data from: Grow where you thrive, or where only you can survive? An analysis of performance curve evolution in a clade with diverse habitat affinities
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Figs. 1–4. Species Accumulation Curves. 1 in Diversity of Two Beetle Taxa at a Western Amazonian Locality (Coleoptera: Histeridae; Staphylinidae, Pselaphinae)
Figs. 1–4. Species Accumulation Curves. 1) All methods combined; 2) Berlese samples;
FIGURE 7. Species accumulation curves using ACE and CHAO1 in Diversity of Myxomycetes from Peru Part III: The high Andes and the altiplano
FIGURE 7. Species accumulation curves using ACE and CHAO1.
FIG. 2. Species accumulation curves for all 10 in Bat diversity in the Simandou Mountain Range of Guinea, with the description of a new white-winged vespertilionid
FIG. 2. Species accumulation curves for all 10 sites surveyed at Simandou (2002 RAP and 2008 baseline) generated with EstimateS 8.2.0. Also shown are the Chao1 (dashed line = 42.15) and Chao 2 (dotted line = 45.03), conservative estimates of the total number of species to be expected at Simandou
Fig. 6 Rarefaction curves for Stations 325 in Community structure and species diversity of Harpacticoida (Crustacea: Copepoda) at two sites in the deep sea of the Angola Basin (Southeast Atlantic)
Fig. 6 Rarefaction curves for Stations 325 (green) and 346 (red). E(SN) Number of expected species, N individuals. Dotted lines Expected species numbers for 340 individuals
FIGURE 2. Rarefaction curve. The curve was calculated with a 95 in Bryofloristic diversity and conservation value of a protected area in the Brazilian semi-arid region
FIGURE 2. Rarefaction curve. The curve was calculated with a 95% confidence level, considering all collections throughout the two seasons, rainy and dry.
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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)
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.