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Electronic Supplementary Material for: The biogeography of population resilience in lowland South America
<p>## Electronic Supplementary Information for "The biogeography of population resilience in lowland South America" chapter</p> <p>This repository holds the code and data for reproducing the analysis in the chapter "The biogeography of population resilience in lowland South America", forthcoming in the Oxford Handbook of Resilience in Climate History. It also contains supplementary information on the statistical modelling undertaken as part of the aforementioned work. </p> <p>The code comprises five principal sections, plus setup:</p> <p>0. Setup & data loading<br>1. Data processing and display<br>2. Radiocarbon analysis<br>3. Statistical modelling<br>4. Output<br>5. Supplementary output</p> <p>The code features an extended version of the p2pPerm function: (https://github.com/philriris/p2pPerm) that was first introduced in Riris and De Souza (2021) (https://doi.org/10.3389/fevo.2021.740629), here called the `resmet` (RESilience METrics) function. </p> <p>In addition, the code is accompanied by three datasets:</p> <p>- A table containing archaeological radiocarbon dates from lowland tropical South America <br>- A shapefile of South American ecoregions, original data available here: http://ecologicalregions.info/data/sa/<br>- A table of domesticated Neotropical plants resolved in Amazonian palaeoecological records, after Iriarte et al. (2020)(https://doi.org/10.1016/j.quascirev.2020.106582)</p> <p>Briefly, the georeferenced radiocarbon data used in the paper have been compiled from a wide range of sources, including Goldberg et al. (2016), Riris & Arroyo-Kalin (2019), Napolitano et al. (2019) Arroyo-Kalin & Riris (2021), De Souza & Riris (2021), and Bird et al. (2022). These sources have been extensively cross-checked for duplicate lab codes, variation in site naming conventions, and reported locations, in order to minimise errors arising from these variables. It does not purport to be error-free, although it is adequate for the current analysis. </p> <p>As well as its use in the production of Figure 1, the ecoregions shapefile has been intersected with the radiocarbon date locations to append this information to `rhdata.csv`. An extended description and rationale for its use can be found in the main text. </p> <p>For the convenience of the end-user, an additional file containing the main results (metrics_regular.csv) is included. The data contained in this table is the subject of section 3 of the code, Statistical Modelling. It forms the basis of the discussion in the chapter. </p> <p>Data cleaning was carried out manually on the raw output of the `resmet` function to remove false positives from the table. These "events" are either: a) statistically significant downturns present in periods where, logically, no humans should be present, e.g. in the Greater Antilles before ~6000 cal BP, or: b) downturns where there are no minima in the summed probability distributions of calibrated radiocarbon dates, returning nonsensical resilience metrics. Removing these data rows introduces errors to the variable Cumulative, which counts the cumulative number of downturns detected by the `permTest` function in `rcarbon`. The file version of the output in this repository should be considered authoritative for present purposes, as these counting errors in Cumulative have been manually fixed too. </p> <p>### References</p> <p>- Arroyo-Kalin, M. and Riris, P. 2021. Did pre-Columbian populations of the Amazonian biome reach carrying capacity during the Late Holocene? *Phil. Trans. R. Soc. B* 376: 20190715 http://doi.org/10.1098/rstb.2019.0715</p> <p>- Bird, D., Miranda, L., Vander Linden, M., Robinson, E., Bocinsky, R.K., Nicholson, C., Capriles, J.M., Finley, J.B., Gayo, E.M., Gil, A. and d’Alpoim Guedes, J. 2022. p3k14c, a synthetic global database of archaeological radiocarbon dates. *Scientific Data* 9: 1-19. https://doi.org/10.1038/s41597-022-01118-7</p> <p>- De Souza, J.G. & Riris, P. 2021. Delayed demographic transition following the adoption of cultivated plants in the eastern La Plata Basin and Atlantic coast, South America. *Journal of Archaeological Science*. 125: 105293. https://doi.org/10.1016/j.jas.2020.105293</p> <p>- Goldberg, A., Mychajliw, A.M. and Hadly, E.A. 2016. Post-invasion demography of prehistoric humans in South America. *Nature* 532: 232-235. https://doi.org/10.1038/nature17176 </p> <p>- Iriarte, J., Elliott, S., Maezumi, S.Y., Alves, D., Gonda, R., Robinson, M., de Souza, J.G., Watling, J. and Handley, J. 2020. The origins of Amazonian landscapes: Plant cultivation, domestication and the spread of food production in tropical South America. _Quaternary Science Reviews_ 248: 106582. https://doi.org/10.1016/j.quascirev.2020.106582 </p> <p>- Napolitano MF, DiNapoli RJ, Stone JH, Levin MJ, Jew NP, Lane BG, O’Connor JT, Fitzpatrick SM. 2019. Reevaluating human colonization of the Caribbean using chronometric hygiene and Bayesian modeling. _Science Advances_. 5: eaar7806. https://doi.org/10.1126/sciadv.aar7806</p> <p>- Riris, P. and Arroyo-Kalin, M. 2019. Widespread population decline in South America correlates with mid-Holocene climate change. *Scientific Reports* 9: 6850. https://doi.org/10.1038/s41598-019-43086-w</p> <p>- Riris, P. and De Souza, J.G. 2021. Formal tests for resistance-resilience in archaeological time series. _Frontiers in Ecology and Evolution_, 9. https://doi.org/10.3389/fevo.2021.740629</p> <p> </p>
Central America climate adaptation corridor analysis
<p>This repository contains data and code used for a regional-scale analysis of potential climate adaptation corridors and their ecological characteristics and conservation status in Central America. This repository is associated with a manuscript "Mapping climate adaptation corridors for biodiversity - A regional-scale case study in Central America" by McCullough et al. At the time that this repository was archived, the paper had not yet been accepted at PLOS One, but hopefully it is by the time anyone is reading this. The readme inside the repository provides an overview of subdirectories and R scripts. This is an updated version of a beta version repository that had less documentation and did not contain some of the larger GIS files. This research was supported by the International Conservation Fund of Canada, the Bobolink Foundation, the BAND Foundation, and the Gordon and Betty Moore Foundation.</p>
Bumble bee responses to climate and landscapes: Investigating habitat associations and species assemblages across geographic regions in the United States of America
<p><span>Bumble bees are integral pollinators of native and cultivated plant communities, but species are undergoing significant changes in range and abundance on a global scale. Climate change and land cover alteration are key drivers in pollinator declines; however, limited research has evaluated the cumulative effects of these factors on bumble bee<em> </em>assemblages. This study tests bumble bee assemblage (calculated as richness and abundance) responses to climate and land use by <span>modeling </span>species-specific habitat requirements, and assemblage-level responses across geographic regions. <span>We integrated species richness, abundance, and distribution data for 18 bumble bee species with site-specific bioclimatic, landscape composition, and landscape configuration data to evaluate</span> the effects of multiple environmental stressors <span>on bumble bee assemblages throughout</span> 433 agricultural fields in<span> Florida, Indiana, Kansas, Kentucky, Maryland, South Carolina, Utah, Virginia, and West Virginia from 2018 to 2020. Distinct east vs. west groupings emerged when evaluating species-specific habitat associations, prompting a detailed evaluation of bumble bee assemblages by geographic region. Maximum temperature of warmest month and precipitation of driest month had a positive impact on bumble bee assemblages in the Corn Belt/Appalachian/northeast, southeast, and northern plains regions, but a negative impact in the mountain region. Further, </span>forest land cover surrounding agricultural fields was highlighted as supporting more rich and abundant bumble bee assemblages<span>. Overall, climate and land use combine to drive bumble bee assemblages, but how those processes operate is idiosyncratic and spatially contingent across regions. From these findings, we suggested regionally specific management practices to best support rich and abundant bumble bee assemblages in agroecosystems. </span>Results from this study contribute to a better understanding of climate and landscape factors affecting bumble bees and their habitats throughout the USA. </span></p>
FIG. 30 in Description of eight new species of the genus Acanthocephala Laporte, 1833 from South America (Hemiptera, Coreidae, Acanthocephalinae)
FIG. 30. — Distributional map of the new species of the genus Acanthocephala Laporte, 1833 described in the present work.
FIG. 29 in Description of eight new species of the genus Acanthocephala Laporte, 1833 from South America (Hemiptera, Coreidae, Acanthocephalinae)
FIG. 29. — Acanthocephala pleuritica, male genitalia: A, pygophore lateral; B, pygophore dorsal; C, right paramere, lateral views; D, aedeagus. Abbreviations: see Material and methods. Scale bars: A, B, 1 mm; C, D, 0.5 mm.
FIG. 27 in Description of eight new species of the genus Acanthocephala Laporte, 1833 from South America (Hemiptera, Coreidae, Acanthocephalinae)
FIG. 27. — Acanthocephala pleuritica: A, male, dorsal view; B, female, dorsal view; C, head, dorsal view; D, head, lateral view; E, pronotum. Scale bars: A, B, 10 mm; C, D, 1 mm; E, 2 mm.
FIG. 28 in Description of eight new species of the genus Acanthocephala Laporte, 1833 from South America (Hemiptera, Coreidae, Acanthocephalinae)
FIG. 28. — Acanthocephala pleuritica: A, male lateral view; B, metathoracic gland and evaporatory area; C, male metafemur and metatibia; D, female metafemur and metatibia; E, lateral expansions on abdominal sternites III, ventral view. Abbreviations: see Material and methods. Scale bars: A, C, D, 5 mm; B, 0.5 mm; E, 1 mm.
FIG. 25. — Acanthocephala spinosa n in Description of eight new species of the genus Acanthocephala Laporte, 1833 from South America (Hemiptera, Coreidae, Acanthocephalinae)
FIG. 25. — Acanthocephala spinosa n. sp.: A, metathoracic gland and evaporatory area; B, male metafemur; C, male metatibia; D, lateral expansions on abdominal sternite III, ventral view. Abbreviations: see Material and methods. Scale bars: A, 0.5 mm; B, C, 5 mm; D, 1 mm.
FIG. 26. — Acanthocephala spinosa n in Description of eight new species of the genus Acanthocephala Laporte, 1833 from South America (Hemiptera, Coreidae, Acanthocephalinae)
FIG. 26. — Acanthocephala spinosa n. sp., male genitalia: A, pygophore lateral; B, pygophore dorsal; C, right paramere, lateral views; D, aedeagus. Abbreviations: see Material and methods. Scale bars: A, B, 1 mm; C, D, 0.5 mm.
FIG. 23. — Acanthocephala pilosa n in Description of eight new species of the genus Acanthocephala Laporte, 1833 from South America (Hemiptera, Coreidae, Acanthocephalinae)
FIG. 23. — Acanthocephala pilosa n. sp., male genitalia: A, pygophore lateral; B, pygophore dorsal; C, right paramere, lateral views; D, aedeagus. Abbreviations: see Material and methods. Scale bars: A, B, D, 1 mm; C, 0.5 mm.
FIG. 24. — Acanthocephala spinosa n in Description of eight new species of the genus Acanthocephala Laporte, 1833 from South America (Hemiptera, Coreidae, Acanthocephalinae)
FIG. 24. — Acanthocephala spinosa n. sp., male holotype: A, dorsal view; B, ventral view; C, head, dorsal view; D, head, lateral view; E, pronotum. Scale bars: A, B, 10 mm; C, D, 1 mm; E, 2 mm.
FIG. 22. — Acanthocephala pilosa n in Description of eight new species of the genus Acanthocephala Laporte, 1833 from South America (Hemiptera, Coreidae, Acanthocephalinae)
FIG. 22. — Acanthocephala pilosa n. sp.: A, pronotum; B, metathoracic gland and evaporative area; C, male metafemur; D, male metatibia; E, abdominal tergites in dorsal view. Abbreviations: see Material and methods. Scale bars: A, C-E, 5 mm; B, 0.5 mm.
FIG. 13. — Acanthocephala flaviantennata n in Description of eight new species of the genus Acanthocephala Laporte, 1833 from South America (Hemiptera, Coreidae, Acanthocephalinae)
FIG. 13. — Acanthocephala flaviantennata n. sp., male genitalia: A, pygophore lateral; B, pygophore dorsal; C, right paramere, lateral views; D, aedeagus. Abbreviations: see Material and methods. Scale bars: A, B, D, 1 mm; C, 0.5 mm.
FIG. 16. — Acanthocephala harryi n in Description of eight new species of the genus Acanthocephala Laporte, 1833 from South America (Hemiptera, Coreidae, Acanthocephalinae)
FIG. 16. — Acanthocephala harryi n. sp.: A, pronotum; B, metathoracic gland and evaporative area; C, female metafemur; D, female metatibia. Abbreviations: see Material and methods. Scale bars: A, 2 mm; B-D, 5 mm.
FIG. 15. — Acanthocephala harryi n in Description of eight new species of the genus Acanthocephala Laporte, 1833 from South America (Hemiptera, Coreidae, Acanthocephalinae)
FIG. 15. — Acanthocephala harryi n. sp., female holotype: A, dorsal view; B, ventral view; C, head, dorsal view; D, head, lateral view. Scale bars: A, B, 10 mm; C, D, 1 mm.
FIG. 9. — Acanthocephala carioca n in Description of eight new species of the genus Acanthocephala Laporte, 1833 from South America (Hemiptera, Coreidae, Acanthocephalinae)
FIG. 9. — Acanthocephala carioca n. sp.: A, metathoracic gland and evaporatory area; B, male metafemur; C, male metatibia; D, lateral expansions on abdominal sternite III. Abbreviations: see Material and methods. Scale bars: A, 0.5 mm; B, C, 5 mm; D, 1 mm.
FIG. 1. — Acanthocephala angusta n in Description of eight new species of the genus Acanthocephala Laporte, 1833 from South America (Hemiptera, Coreidae, Acanthocephalinae)
FIG. 1. — Acanthocephala angusta n. sp.: A, male holotype, dorsal view; B, female paratype, dorsal view; C, head, dorsal view; D, head, lateral view; E, pronotum. Scale bars: A, B, 10 mm; C, D, 1 mm; E, 2 mm.
FIG. 5. — Acanthocephala brunnea n in Description of eight new species of the genus Acanthocephala Laporte, 1833 from South America (Hemiptera, Coreidae, Acanthocephalinae)
FIG. 5. — Acanthocephala brunnea n. sp., female holotype: A, dorsal view; B, ventral view; C, head, dorsal view; D, head, lateral view. Scale bars: A, B, 10 mm; C, D, 1 mm.
FIG. 4.— Acanthocephala angusta n in Description of eight new species of the genus Acanthocephala Laporte, 1833 from South America (Hemiptera, Coreidae, Acanthocephalinae)
FIG. 4.— Acanthocephala angusta n. sp., female genitalia: A, pregenital and genital segments; B, first gonocoxa and gonapophysis; C, second gonocoxa and gonapophysis; D, spermatheca. Abbreviations: see Material and methods. Scale bars: A, 2 mm; B, 1 mm; C, D, 0.5 mm.
FIG. 12. — Acanthocephala flaviantennata n in Description of eight new species of the genus Acanthocephala Laporte, 1833 from South America (Hemiptera, Coreidae, Acanthocephalinae)
FIG. 12. — Acanthocephala flaviantennata n. sp.: A, metathoracic gland and evaporatory area; B, male metafemur; C, female metafemur; D, male metatibia; E, female metatibia; F, lateral expansions on abdominal sternite III, ventral view. Abbreviations: see Material and methods. Scale bars: A, 1 mm; B-E, 5 mm; F, 1 mm.
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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.