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Supplementary material for: Testing the peak-end rule in bumblebees: lack of preference for a higher-reward sequence when the final reward is disappointing

<h3>ABSTRACT</h3> The peak-end rule describes the tendency to evaluate experiences by their most intense and final moments, rather than considering the entire experience as a whole. While this cognitive bias is well-established in humans, studies on nonhuman animals are very limited. Bumblebees make foraging decisions largely based on past experiences, but whether peak-end effects influence their subsequent flower choices is still unknown. Here, we trained individual Bombus terrestris workers on two artificial flower types, blue and yellow, over 12 consecutive foraging bouts. One flower type offered a sequence of three high-quality rewards (25 μL drops of 50% w/w sucrose solution: “consistent” sequence), while the other provided the same sequence but ended with an additional, lower-quality reward (25 μL drop of 20% w/w sucrose solution: “poor end” sequence). We then tested the bees' flower type preference in a final binary choice. Bees showed a strong preference for blue flowers, both in their initial and overall visits. Across all visits during a 1-minute period, they also favoured flowers associated with the “consistent” sequence, though this preference was significant only when these flowers were yellow. Interestingly, despite offering more sucrose per foraging bout, bees did not favour the “poor end” sequence flower. This study is, to our knowledge, the first to investigate peak-end effects in an insect. How bees evaluate sequential rewards when foraging remains largely unexplored, yet could provide valuable insights into nectar distribution and plant-pollinator co-evolution.

Pterula and Pterulicium concatenated sequence alignment

Open the record for dataset details and reuse information.

Figure 2 in Examining metrics and magnitudes of molecular genetic differentiation used to delimit cetacean subspecies based on mitochondrial DNA control region sequences

Figure 2. Relationship between ΦST and Nei's estimate of net divergence (dA) among cetacean population, subspecies, and species pairs estimated using mitochondrial DNA control region sequence data. Specific values mentioned in the text are numbered: 1 = Neophocaena species; 2 = killer whale populations. The three green squares in the left-hand side of the figure (ΦST <0.07) represent, from bottom to top, the subspecies comparisons for S. attenuata, S. longirostris, and L. obscurus, respectively.

Figure 1 in Examining metrics and magnitudes of molecular genetic differentiation used to delimit cetacean subspecies based on mitochondrial DNA control region sequences

Figure 1. Box and whisker plots showing median and 1st and 3rd quartiles, and minimum and maximum values for six metrics of genetic divergence among cetacean population, subspecies, and species pairs estimated using mitochondrial DNA control region sequence data.

Figure 7 in Diagnosability of mtDNA with Random Forests: Using sequence data to delimit subspecies

Figure 7. Summary of Random Forests classifications for each empirical comparison. Each row shows results from the stratum with the smallest fraction of individuals correctly classified, with comparisons labeled by their taxonomic codes as listed in Table 2. Colors identify comparison type as species (blue), subspecies (green), and populations (red). Points show the fraction of individuals correctly classified with probabilities> 50% (PD50, circles), and> 95% (PD95, triangles). Thin colored lines show 95% confidence intervals (CI) around PD50 estimates. Gray bars show range of a priori random classification rates based on individual size (left) to maximum possible classification rates based on shared haplotypes (right).

Figure 6 in Diagnosability of mtDNA with Random Forests: Using sequence data to delimit subspecies

Figure 6. Frequency distributions of the change in observed diagnosability (x-axis) in the simulated data for increasing levels of the probability of misstratification (vertical panels). Figures on the left and right columns are censored by data sets for original diagnosability ≤50% and>50%, respectively.

Figure 5 in Diagnosability of mtDNA with Random Forests: Using sequence data to delimit subspecies

Figure 5. Two-dimensional GAM fits of theta (Ɵ), number of migrants (Nem), and divergence time in generations (T) from Model 2 simulated data. From left to right, columns show results from models without migration (m = 0), with migration and Nem <1, and Nem ≥ 1. Colors indicate model prediction of percent correctly classified.

Figure 4 in Diagnosability of mtDNA with Random Forests: Using sequence data to delimit subspecies

Figure 4. GAM fit of number of migrants (Nem) from Model 2 parameters. Solid line shows median value of predicted percent correctly classified, and shaded area shows 95% CI. The switch from bimodal distribution to a normal distribution occurs at Nem = 1 (log10Nem = 0).

Figure 3 in Diagnosability of mtDNA with Random Forests: Using sequence data to delimit subspecies

Figure 3. Two-dimensional GAM fits of effective population size (Ne), divergence time in generations (T), and mutation rate (µ) from Model 1 simulated data. Results from models without migration to the left and those with migration to the right. Colors indicate model prediction of percent correctly classified.

Figure 1 in Diagnosability of mtDNA with Random Forests: Using sequence data to delimit subspecies

Figure 1. (A) Distribution of a hypothetical character for two putative subspecies (red and blue) demonstrating minimum overlap necessary to satisfy 75% rule of Amadon (1949). Character is continuous on the x-axis. Dashed lines indicate the point at which 75% of each distribution is outside of 99%+ of the other. Solid line indicates point of overlap where 97% of both distributions are outside one another. (B) Probability of membership to subspecies for specimens having values along the character axis. Probability is based on the ratio of the distribution frequencies at each point along the x-axis, with a 50:50 probability occurring at the threshold point.

Reference Sequence Library Resources - Maine-eDNA

The following files and resources are associated with the Maine-eDNA Reference Library Research Group - aiming to create reference sequence library resources for researchers part of Maine-eDNA or otherwise interested in leveraging eDNA tools for research in the Gulf of Maine. These include: <ul> <li> RoughWorkflow.zip </li> <ul> <li> contains a NCBI scraping script to build reference databases based on an input species list, a configuration file for the script, and genbankr version - most useful for shorter species lists (time-intensive) </li> </ul> <li> 12S_REFDB.fasta </li> <ul> <li> A DADA2-compliant reference library for 12S sequences, built with the RoughWorkflow based on the GNRMaineSpecies_May2024 species list </li> </ul> <li> COI_REFDB.fasta </li> <ul> <li> A DADA2-compliant reference library for COI sequences, built with the RoughWorkflow based on the GNRMaineSpecies_May2024 species list </li> </ul> <li> GitHub Repo - referee - <a href="https://github.com/BigelowLab/referee">https://github.com/BigelowLab/referee</a> </li> <ul> <li> Scripts for building reference databases for the Maine-eDNA project through downloading GenBank - this workflow is suggested especially for large species lists </li> </ul> <li> GitHub Repo - refdbtools - <a href="https://github.com/BigelowLab/refdbtools">https://github.com/BigelowLab/refdbtools</a>  </li> <ul> <li> R language package to assist in making eDNA reference databases </li> </ul> <li> SpeciesListMeta_Shareable.xlsx </li> <ul> <li> Describes the sources from which the GNRMaineSpecies_May2024.csv and GNRMaineTaxonomiedSpecies_May2024.csv species lists were compiled - sources not associated with a link were found as separate files and are hosted elsewhere. Species list sources are courtesy of public datasets, Maine-eDNA researchers, and collaborators </li> </ul> <li> GNRMaineSpecies_May2024.csv </li> <ul> <li> A Maine (and surrounding area) species list ran through taxize’s gnr_resolve to resolve species names and fill out taxonomy (full results) </li> </ul> <li> GNRMaineTaxonomiedSpecies_May2024.csv </li> <ul> <li> A Maine (and surrounding area) species list ran through taxize’s gnr_resolve to resolve species names and fill out taxonomy (only species results that could be resolved with taxonomy) </li> </ul> </ul>   Contact Beth Y. Davis - bethy.davis4@gmail.com for questions   ### Changelog: May 16, 2023 (Version 1.0) - Initial upload July 11, 2023 (Version 1.1) - Did additional cleaning to the MaineSpeciesList_Clean file and uploaded the new version - July2023_SpeciesList May 20, 2024 (Version v3) -  Additional cleaning to correct deduplication errors and ran taxize's gnr_resolve to resolve names and fill out taxonomy. The version update adds two files, GNRMaineSpecies_May2024.csv containing the full result of gnr_resolve, and GNRMaineTaxonomiedSpecies_May2024.csv contains only those species from the original list that could be resolved with taxonomy. The SpeciesListMeta_Shareable.csv has not been updated but is still an accurate tracker of the sources from which the species names were gained from. November 27, 2024 (Version 4.0) - Updated Zenodo description and added the RoughWorkflow R files, 12S and COI files

Concatenated alignment of 12S and 16S sequences of the genus Pristimantis.

A new species of Pristimantis (Amphibia, Anura, Strabomantidae) from a montane forest of the Pui Pui Protected Forest in central Peru

opencc-zeroOct 2024View details →

Sequencing a botanical monument: a chromosome-level assembly of the 400-year-old Goethe's Palm (Chamaerops humilis L.) at the Botanical Garden of the University of Padua (Italy)

The enclosed data pertains to the genome assemblies of the mitochondrion (final_mitogenome.fasta) and the plastid (plastid_genome.fasta) of the dwarf palm Chamaerops humilis L. Please refer to the published paper for further details.

opencc-by-4.0Sep 2024View details →

common function paralog pairs and sequence similarity features

This repository contains datasets of selected paralog pairs (Ensembl 111), labeled with various "common function" annotations, including PPI, SL, and GO datasets for both human (Homo sapiens) and budding yeast (Saccharomyces cerevisiae). These paralog pairs are characterized using different sequence similarity features, such as AlphaFold-predicted structures, Protein Language Model embeddings, and similarity searches from various databases.   These datasets are used in the following manuscript: <a href="https://www.biorxiv.org/content/10.1101/2024.10.11.617835v1">Evaluating Sequence and Structural Similarity Metrics for Predicting Shared Paralog Functions</a>   For the corresponding analysis notebooks, see: <a href="https://github.com/cancergenetics/paralog_seq_similarity/tree/main">github.com/cancergenetics/paralog_seq_similarity/</a>

dryad40/100

Green turtle ddRAD raw sequencing data

The occasional westward transport of warm water of the Agulhas Current, 'Agulhas leakage', around southern Africa has been suggested to facilitate tropical marine connectivity between the Atlantic and Indian oceans, but the 'Agulhas leakage' hypothesis doesn't explain the signatures of eastward gene flow observed in many tropical marine fauna. We investigated an alternative hypothesis: the establishment of a warm-water corridor during comparatively warm interglacial periods. The 'warm-water corridor' hypothesis was investigated by studying the population genomic structure of Atlantic and Southwest Indian Ocean green turtles (N = 27) using 12,035 genome-wide single nucleotide polymorphisms (SNPs) obtained via ddRAD sequencing. Model-based and multivariate clustering suggested a hierarchical population structure with two main Atlantic and Southwest Indian Ocean clusters, and a Caribbean and East Atlantic sub-cluster nested within the Atlantic cluster. Coalescent-based model selection supported a model where Southwest Indian Ocean and Caribbean populations diverged from the East Atlantic population during the transition from the last interglacial period (130 – 115 thousand years ago; kya) to the last glacial period (115 – 90 kya). The onset of the last glaciation appeared to isolate Atlantic and Southwest Indian Ocean green turtles into three refugia, which subsequently came into secondary contact in the Caribbean and Southwest Indian Ocean when global temperatures increased after the Last Glacial Maximum. Our findings support the establishment of a warm-water corridor facilitating tropical marine connectivity between the Atlantic and Southwest Indian Ocean during warm interglacials.

opencc-zeroDec 2020View details →

opencc-by-4.0Oct 2021View details →

RxLR EFFECTROR SEQUENCES ALONG WITH GFF3 FILES

Fasta sequences are the RxLR sequences of all 128 Phytophthora genomes. The GFF3 files contain the coordinates of the RxLRs.

opencc-by-4.0Oct 2021View details →

Data supporting "Slowest-first translation scheme: Structural asymmetry along protein sequences and co-translational folding"

Contains data for a set of 16,200 non-redundant protein structures taken from the Protein Data Bank. Associated code can be found at https://github.com/jomimc/FoldAsymCode.

opencc-zeroSep 2021View details →

FIG. 3 in Foraminiferal biostratigraphy, facies and sequence stratigraphy analysis across the K-Pg Boundary in Hazara, Lesser Himalayas (Dhudial Section)

FIG. 3. — Lithostratigraphic column showing the lithology, constituents and facies of the Dhudial Section.

opencc-zeroSep 2021View details →

FIG. 5 in Foraminiferal biostratigraphy, facies and sequence stratigraphy analysis across the K-Pg Boundary in Hazara, Lesser Himalayas (Dhudial Section)

FIG. 5. — Photomicrographs of thin slides: A, Planktonic foraminifera () and ferroan euhedral dolomite crystals () (S# 09); B, dolomitized zone () with planktonic foraminifera (), radiolarians () and ostracods () (S# 25); C, contact of the foraminiferal wackestone and packstone patch (S# 22); D, radiolarian rich facies showing radiolarians () and calcispheres () (S# 18); E, Heterohelix sp. () along with radiolarians () and ostracods () (S# 17); F, dolomitized zone between the lime mudstone (MS) and wackestone (WS) (S# 11); G, tectonic induced fracturing (S# 07); H, radiolarians () and zoned ferroan dolomite crystals in the mudstone facies (S# 11); I, well rounded feldspar grains (S# 38); J, chert lithic (S# 36); K, ferruginous matrix (S# 37); L, quartz () and feldspar () grains in Hangu Formation (S# 38). Scale bars: A-C, F, G, 200 µm; D, E, H-L, 100 µm.