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23 results for “species accumulation curves”
Fig. 2. Species accumulation curves for 45 in Mammal inventories in Seasonal Neotropical Forests: traditional approaches still compensate drawbacks of modern technologies
Fig. 2. Species accumulation curves for 45 sampling days for each method used to sample mammals in Serra do Japi Biological Reserve, JundiaÍ, State of SÃo Paulo, Brazil in July and August 2009 and January and February 2010.
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
Fig. 1. Species accumulation curves for 2012 pitfall sampling with each facet representing a in Changes in an invaded Florida ant (Hymenoptera: Formicidae) community over 25 years
Fig. 1. Species accumulation curves for 2012 pitfall sampling with each facet representing a distinct habitat. Curves were generated with incidence data and sites were added randomly to generate these curves. Shaded areas represent the 95% confidence intervals.
Fig. 2. Species accumulation curves for 2012 litter sampling with each facet representing a in Changes in an invaded Florida ant (Hymenoptera: Formicidae) community over 25 years
Fig. 2. Species accumulation curves for 2012 litter sampling with each facet representing a distinct habitat. Curves were generated with incidence data and sites were added randomly to generate these curves. Shaded areas represent the 95% confidence intervals.
Fig. 3. Species accumulation curves for 2018 in Changes in an invaded Florida ant (Hymenoptera: Formicidae) community over 25 years
Fig. 3. Species accumulation curves for 2018 pitfall sampling done in sandhill habitat. Monthly sampling data was pooled into a single dataset. The curve was generated with incidence data and sites were added randomly to generate the curve. Shaded areas represent the 95% confidence intervals.
Figure 4. Species accumulation curve for species richness and a in Food habits of the Cougar Puma concolor (Carnivora: Felidae) in the Central Andes of the Colombian Coffee Region
Figure 4. Species accumulation curve for species richness and a non-parametric estimator of species richness Chao 2 at several sectors of the Central Andes of the Coffee Region in Colombia. The species accumulation curve indicates that the sampling shows 100% of the species consumed by the cougar in this area.
Figure 2. Bird species accumulation curve and estimated richness curve obtained from the Chao 1 in Avifauna of the region of the Volta Grande Hydroelectric Power Plant in Southeast Brazil
Figure 2. Bird species accumulation curve and estimated richness curve obtained from the Chao 1 index for the study area located throughout the reservoir of the Volta Grande Hydroelectric Power Plant in Southeast Brazil. Vertical bars represent the standard deviation of the estimate.
Fig. 4 in Species Accumulation Curves And Similarity Traits Of A Species-Rich Fly (Diptera) Community
Fig. 4. Jackknifed NESS indices relating to the first and kth group of 50, k = 1,2,…, 20
Fig. 3 in Species Accumulation Curves And Similarity Traits Of A Species-Rich Fly (Diptera) Community
Fig. 3. Quasi individual-based species accumulation curves after normalisation
Fig. 1 in Species Accumulation Curves And Similarity Traits Of A Species-Rich Fly (Diptera) Community
Fig. 1. Sample-based species accumulation curves without normalisation
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: Assemblage Accumulation Curves: A framework for resolving species accumulation in biological communities using chloroplast genome sequences
The timing and tempo of the processes involved in community assembly are of substantial concern to community ecologists and conservation managers. The fossil record is a valuable source of data for studying past changes in community composition, but it is not always detailed enough to allow the process of community assembly to be resolved at regional or site scales while tracing the trajectories of known species with associated known traits. We present a three‐step framework for studying present‐day species accumulation through time: DNA sampling from multiple individuals from multiple species within a community; estimates of coalescence times for each species using molecular dating methods; and plotting the accumulation of present‐day species through time using the inferred population ages. Our approach is illustrated using whole chloroplast genomes from plants from three rainforest communities in eastern Australia. Expected times to coalescence for multiple species in each community were inferred from pooled high‐throughput sequence libraries. Local assemblage accumulation curves for each community were constructed. We also explored the variation in assemblage accumulation curves of species with different functional traits. Models of equilibrium species richness informed our null hypothesis and largely explained the shape of the assemblage accumulation curves and indicated that the complexities of the accumulation process should be explored with additional parameters, for example allowing species classes with different extinction rates. The assemblage accumulation curves for the study sites showed evidence of recent population expansions within each of the communities. This signal of recent accumulation is consistent with the increase in suitable rainforest habitat that followed the Last Glacial Maximum. Our method of constructing assemblage accumulation curves provides a simple approach for visualizing species‐accumulation data. It can be used to test hypotheses such as the relative survival potential of species‐specific ecological attributes. Although our example used single‐nucleotide polymorphisms derived from whole‐chloroplast sequencing, this framework can be applied to mitochondrial genomes and to communities of other organisms.
FIGURE 2. Smoothed species accumulation curves for Paraguayan ants, generated over 50 in A catalogue of the ants of Paraguay (Hymenoptera: Formicidae)
FIGURE 2. Smoothed species accumulation curves for Paraguayan ants, generated over 50 sampling iterations on 3,912 randomized species records.
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)
FIGURE 1. Species accumulation curves. A in The crane flies (Diptera: Tipuloidea) of Great Smoky Mountains National Park
FIGURE 1. Species accumulation curves. A) All traps; Limoniidae, Tipulidae. B) Twin Creeks. C) Cades Cove. D) Indian Gap. E) Purchase Knob. F) Cataloochee. Plots in Figs. 1BF are presented as specimens collected in each Malaise trap and for traps at each plot combined.
Data from: Assemblage Accumulation Curves: A framework for resolving species accumulation in biological communities using chloroplast genome sequences
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Data from: Can observation skills of citizen scientists be estimated using species accumulation curves?
Volunteers are increasingly being recruited into citizen science projects to collect observations for scientific studies. An additional goal of these projects is to engage and educate these volunteers. Thus, there are few barriers to participation resulting in volunteer observers with varying ability to complete the project's tasks. To improve the quality of a citizen science project's outcomes it would be useful to account for inter-observer variation, and to assess the rarely tested presumption that participating in a citizen science projects results in volunteers becoming better observers. Here we present a method for indexing observer variability based on the data routinely submitted by observers participating in the citizen science project eBird, a broad-scale monitoring project in which observers collect and submit lists of the bird species observed while birding. Our method for indexing observer variability uses species accumulation curves, lines that describe how the total number of species reported increase with increasing time spent in collecting observations. We find that differences in species accumulation curves among observers equates to higher rates of species accumulation, particularly for harder-to-identify species, and reveals increased species accumulation rates with continued participation. We suggest that these properties of our analysis provide a measure of observer skill, and that the potential to derive post-hoc data-derived measurements of participant ability should be more widely explored by analysts of data from citizen science projects. We see the potential for inferential results from analyses of citizen science data to be improved by accounting for observer skill.
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.
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.