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Electronic Supplementary Material for: The biogeography of population resilience in lowland South America

## Electronic Supplementary Information for "The biogeography of population resilience in lowland South America" chapter 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.  The code comprises five principal sections, plus setup: 0. Setup & data loading 1. Data processing and display 2. Radiocarbon analysis 3. Statistical modelling 4. Output 5. Supplementary output 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.  In addition, the code is accompanied by three datasets: - A table containing archaeological radiocarbon dates from lowland tropical South America  - A shapefile of South American ecoregions, original data available here: http://ecologicalregions.info/data/sa/ - 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) 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.  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.  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.  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.  ### References - 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 - 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 - 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 - 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  - 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  - 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 - 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 - 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

Data from: Genome-wide association mapping within a local Arabidopsis thaliana population more fully reveals the genetic architecture for defensive metabolite diversity

A paradoxical finding from genome-wide association studies (GWAS) in plants is that variation in metabolite profiles typically maps to a small number of loci, despite the complexity of underlying biosynthetic pathways. This discrepancy may partially arise from limitations presented by geographically diverse mapping panels. Properties of metabolic pathways that impede GWAS by diluting the additive effect of a causal variant, such as allelic and genic heterogeneity and epistasis, would be expected to increase in severity with the geographic range of the mapping panel. We hypothesized that a population from a single locality would reveal an expanded set of associated loci. We tested this in a French Arabidopsis thaliana population (< 1 km transect) by profiling and conducting GWAS for glucosinolates, a suite of defensive metabolites that have been studied in depth through functional and genetic mapping approaches. For two distinct classes of glucosinolates, we discovered more associations at biosynthetic loci than previous GWAS with continental-scale mapping panels. Candidate genes underlying novel associations were supported by concordance between their observed effects in the TOU-A population and previous functional genetic and biochemical characterization. Local populations complement geographically diverse mapping panels to reveal a more complete genetic architecture for metabolic traits.

opencc-by-4.0Jan 2016View details →

Fig. 1 in SHORT COMMUNICATION Monitoring a population of Cruziohyla craspedopus (Funkhouser, 1957) using an artificial breeding habitat

Fig. 1. Site map for ABHab points at LPS: dashed line is approximate separation of terra firma and flood plain forest.

Data from: Winter-moth populations are isolated on co-occurring tree species with contrasting budburst-phenology

Differences between neighbouring tree species in phenology could isolate populations of host-plant generalists that depend on matching the phenology of their host. We studied the relationship between the budburst phenology of two co-occurring tree species with early (hornbeams) and late (oaks) budburst, and the egg-hatching date of associated winter moths (Operophtera brumata) during two seasons (autumns starting in 2020 and 2021). A previous study in spring 2019 had found no winter moth larvae on the focal oaks, while we found them mainly on oaks with hornbeam neighbours in 2022. Congruently, adult winter moths were rarely encountered during the autumns of 2018 and 2019 and sparsely in 2020 and 2021, and then mainly near hornbeams.The vast majority of winter moths had early-hatching eggs when both hornbeams and oaks were present, matching the hornbeams rather than the oaks. Where hornbeam was absent in the neighbourhood, the few winter moths had late-hatching eggs.These results suggest that winter moth populations on hornbeam and oak are to some degree isolated from each other despite spatial proximity, so that recolonization of oaks by populations synchronized with budburst of hornbeam is slow. Therefore, trees would benefit from differing from their neighbours in budburst phenology with respect to herbivore damage.

Data from: Climate change and population persistence in a hibernating marsupial

Climate change has physiological consequences on organisms, ecosystems, and human societies, surpassing the pace of organismal adaptation. Hibernating mammals are particularly vulnerable as winter survival is determined by short-term physiological changes triggered by temperature. In these animals, winter temperatures cannot surpass certain threshold, above which hibernators arouse from torpor, increasing several fold their energy needs when food is unavailable. Here, we parameterized a numerical model predicting energy consumption in heterothermic species, and modeled winter survival at different climate change scenarios. As a model species, we used the arboreal marsupial monito del monte (genus Dromiciops) which is recognized as one of the few South America hibernators. We modeled four climate change scenarios (from optimistic to pessimistic), based on IPCC projections, predicting that northern and coastal populations (Dromiciops bozinovici) will decline because the minimum number of cold days needed to survive the winter will not be attained. These populations are also the most affected by habitat fragmentation and change in land use. Conversely, Andean and other highland populations at cooler environments, are predicted to persist and thrive. Given the widespread presence of hibernating mammals around the world, models based on simple physiological parameters such as this one, are becoming essential for predicting species responses to warming in the short term.

opencc-by-4.0Feb 2021View details →

Рис. 2. КоΛичество жиΛых гнезΑ ΑаΛьневосточного аиста, учтенных в 2018–2019 гг. по аΑминистративным районам Амурской обΛасти Fig. 2. The number of inhabited Oriental stork nests recorded in 2018–2019 in different administrative districts of the Amur region in Oriental stork (Ciconia boyciana Swinhoe) breeding population survey in the Amur region in 2018-2019

Рис. 2. КоΛичество жиΛых гнезΑ ΑаΛьневосточного аиста, учтенных в 2018–2019 гг. по аΑминистративным районам Амурской обΛасти Fig. 2. The number of inhabited Oriental stork nests recorded in 2018–2019 in different administrative districts of the Amur region

Data from: Longitudinal gut microbiome dynamics in relation to age and senescence in a wild animal population

In humans, gut microbiome (GM) differences are often correlated with, and sometimes causally implicated in, ageing. However, it is unclear how these findings translate in wild animal populations. Studies that investigate how GM dynamics change within individuals, and with declines in physiological condition, are needed to fully understand links between chronological age, senescence, and the GM, but have rarely been done. Here, we use longitudinal data collected from a closed population of Seychelles warblers (Acrocephalus sechellensis) to investigate how bacterial GM alpha diversity, composition, and stability are associated with host senescence. We hypothesised that GM diversity and composition will differ, and become more variable, in older adults, particularly in the terminal year prior to death, as the GM becomes increasingly dysregulated due to senescence. However, GM alpha diversity and composition remained largely invariable with respect to adult age and did not differ in an individual's terminal year. Furthermore, there was no evidence that the GM became more heterogenous in senescent age groups (individuals older than 6 years), or in the terminal year. Instead, environmental variables such as season, territory quality, and time of day, were the strongest predictors of GM variation in adult Seychelles warblers. These results contrast with studies on humans, captive animal populations, and some (but not all) studies on non-human primates, suggesting that GM deterioration may not be a universal hallmark of senescence in wild animal species. Further work is needed to disentangle the factors driving variation in GM-senescence relationships across different host taxa.

Data and code from: Evaluating genomic offset predictions in a forest tree with high population genetic structure

Predicting how tree populations will respond to climate change is an urgent societal concern. An increasingly popular way to make such predictions is the genomic offset (GO) approach, which aims to use genomic and climate data to identify populations that may experience climate maladaptation in the near future. More precisely, GO tries to represent the change in allele frequencies required to maintain the current gene-climate relationships under climate change. However, the GO approach has major limitations and, despite promising validation of its predictions using height data from common gardens, it still lacks broad empirical testing. In the present study, we evaluated the consistency and empirical validity of GO predictions in maritime pine (Pinus pinaster Ait.), a tree species from southwestern Europe and North Africa with a marked population genetic structure. First, gene-climate relationships were estimated using 9,817 SNPs genotyped in 454 trees from 34 populations; and candidate SNPs potentially involved in climate adaptation were identified. Second, GO was predicted using four methods, namely Gradient Forest (GF), Redundancy Analysis (RDA), latent factor mixed model (LFMM) and Generalised Dissimilarity Modeling (GDM), two sets of SNPs (candidate and control SNPs) and five climate general circulation models (GCMs) to account for uncertainty in future climate predictions. Last, the empirical validity of GO predictions was evaluated within a Bayesian framework by estimating the associations between GO predictions and two independent data sources: mortality data from National Forest Inventories (NFI), and mortality and height data from five common gardens in contrasting environments. We found high variability in GO predictions across methods, SNP sets and GCMs. Regarding validation, GO predictions with GDM and GF (and to a lesser extent RDA) based on the candidate SNPs showed the strongest and most consistent associations with mortality rates in common gardens and NFI plots. We found almost no association between GO predictions and tree height in common gardens, most likely due to the overwhelming effect of population genetic structure on tree height in this species. Our study demonstrates the imperative to validate GO predictions with a range of independent data sources before they can be used as informative and reliable metrics in conservation or management strategies.

opencc-by-4.0Mar 2019View details →

Fig. 2 in Genetic diversity of Egyptian populations of the African Common Toad (Sclerophrys regularis, Reuss 1833)

Fig. 2. Phylogenetic tree of African Common Toad, using COI haplotypes based on the Maximum Likelihood method. Numbers refer to localities mentioned in the text: 1. Sharm El-Shaikh; 2. Arish; 3. Ismailia; 4. Damietta; 5. Alexandria; 6. Matrouh; 7. Gharbiya; 8. Cairo; 9. SiwaOasis; 10. Bani Sweif; 11. Menia; 12. Sohag; 13. Qena; 14. Aswan.

Fig. 15 in Expression of green fluorescent protein defines a specific population of lamina II excitatory interneurons in the GRP::eGFP mouse

Fig. 15. Trychosis breviterebratus sp. nov. (A-F, KPM-NK 75758) ― A, lateral habitus; B, dorsal habitus; C, frons, frontal view; D, head and mesosoma, lateral view; E, propodeum, dorsal view; F, right wings.

Fig. 14. Hylophasma luica Sheng, Li in Expression of green fluorescent protein defines a specific population of lamina II excitatory interneurons in the GRP::eGFP mouse

Fig. 14. Hylophasma luica Sheng, Li & Wang, 2019 (A-D, KPM-NK 75813; E, KPM-NK 75821) and Picardiella melanoleuca (Gravenhorst, 1829)(F-J, KPM-NK 75744) ― A, E, F, lateral habitus; B, G, dorsal habitus; C, H, head, frontal view; D, J, right fore wing; I, propodeum, dorsal view.

Fig. 13. Hoplocryptus sumiyona Uchida, 1956 in Expression of green fluorescent protein defines a specific population of lamina II excitatory interneurons in the GRP::eGFP mouse

Fig. 13. Hoplocryptus sumiyona Uchida, 1956 (A-C, KPM-NK 76581) and H. toshimensis sp. nov. (E-I, KPM-NK 75797) ― A, E, dorsal habitus; B, F, head, frontal view; C, head and mesosoma, dorso-lateral view; D, lateral habitus; G, propodeum, dorsal view; H, right fore wing; I. metasoma, dorsal view.

Fig. 12 in Expression of green fluorescent protein defines a specific population of lamina II excitatory interneurons in the GRP::eGFP mouse

Fig. 12. Hoplocryptus pini (Momoi, 1973) (A, D, F, KPM-NK 76578; B, E, KPM-NK 75780; C, G, KPM-NK 75774; H-J, 76580) ― A-C, H, lateral habitus; D, E, I, head, mesosoma and metasoma, dorsal view; F, J, head, frontal view; G, propodeum, dorsso-lateral view.

Fig. 10 in Expression of green fluorescent protein defines a specific population of lamina II excitatory interneurons in the GRP::eGFP mouse

Fig. 10. Hoplocryptus ezoensis sp. nov. (A, C-F, KPM-NK 75799; B, KPM-NK 75802) and Ho. intermedius sp. nov. (G-K, KPM-NK 75806; L, M, KPM-NK 76582) ― A, G, L, lateral habitus; B, C, dorsal habitus; D, H, M, head, frontal view; E, J, propodeum, dorsal (E) and dorso-lateral (J) view; I, head and mesoscutum, dorso-lateral view; K, right fore wing.

Fig. 6. Caenocryptoides convergens Momoi, 1966 in Expression of green fluorescent protein defines a specific population of lamina II excitatory interneurons in the GRP::eGFP mouse

Fig. 6. Caenocryptoides convergens Momoi, 1966 (A-C, TMNH) and Cryptus daidaigaster sp. nov. (D-J, NIAES) ― A, D, I, lateral habitus; B, E, head, mesosoma and metasoma, dorso-lateral view; C, F, J, head, frontal view; G, propodeum, dorsal view; H, right wings.

Fig. 2 in Expression of green fluorescent protein defines a specific population of lamina II excitatory interneurons in the GRP::eGFP mouse

Fig. 2. Aritranis kuro sp. nov. (A, B, KPM-NK 75832); Buathra nipponica sp. nov. (C, D, KPM-NK 75746), Glabridorsum japonicum sp. nov. (E, KPM-NK 75742), Hoplocryptus ezoensis sp. nov. (F, KPM-NK 75799), Ho. intermedius sp. nov. (G, KPM-NK 75804), Ho. japonicus sp. nov. (H, KPM-NK 75771), Ho. maculatus sp. nov. (I, KPM-NK 75790, J, KPM-NK 75781, K, KPM-NK 75783), Hylophasma luica Sheng, Li & Wang, 2019 (L, M, KPM-NK 75813) and Trychosis breviterebratus sp. nov. (N, KPM-NK 75748), females ― A, F, G, H, M, propodeum and T I, dorsal (A, H, M) and lateral (F, G) view; B, E, L, T I, lateral view; C, I, propodeum, lateral (C) and dorsal (I) view; D, T I, lateral view; J, K, area basalis and part of anterior transverse carina of propodeum; N, metasoma, lateral view. Surface sculpture of right half of Fig. 2A omitted.

Fig. 1 in Expression of green fluorescent protein defines a specific population of lamina II excitatory interneurons in the GRP::eGFP mouse

Fig. 1. Aritranis kuro sp. nov. (A, KPM-NK 75838); Buathra nipponica sp. nov. (B, C, KPM-NK 75746), Hoplocryptus ezoensis sp. nov. (D, KPM-NK 75799), Ho. ashoroensis sp. nov. (E, NIAES), Ho. intermedius sp. nov. (F, KPM-NK 75804), Ho. japonicus sp. nov. (G, KPM-NK 75771), Ho. maculatus sp. nov. (H, KPM-NK 75781), Ho. toshimensis sp. nov. (I, KPM-NK 75797), Hylophasma luica Sheng, Li & Wang, 2019 (J, K, KPM-NK 75813) and Trychosis breviterebratus sp. nov. (L-P, KPM-NK 75748), females ― A-D, J-M, head, frontal (A, B, D, J, L), lateral (C) and dorsal (K, M) view; E-I, apical margin of clypeus, frontal view; K, left mandible; O, left fore femur, anterior view; lower part of epicnemial carina, lateral view.

Fig. 5 in Expression of green fluorescent protein defines a specific population of lamina II excitatory interneurons in the GRP::eGFP mouse

Fig. 5. Buathra nipponica sp. nov. (A-G, KPM-NK 75746; H, I, KPM-NK 75747) ― A, lateral habitus; B, mesosoma, lateral view; C, head, mesosoma and metasoma, dorsal view; D, frons, frontal view; E, propodeum, dorsal view; F, right fore wing; G, apex of metasoma, dorso-lateral view.

Fig. 9 in Expression of green fluorescent protein defines a specific population of lamina II excitatory interneurons in the GRP::eGFP mouse

Fig. 9. Gotra elegans sp. nov. (A, C, D, KPM-NK 75822; B, E, KPM-NK 75823) and Hoplocryptus ashoroensis sp. nov. (F-J, NIAES) ― A, C, lateral habitus; B, dorsal habitus; C, H, head, frontal view; D, head, mesoscutum and scutellum, dorsal view; E, I, propodeum, dorsal view; J, right fore wing.

Fig. 4. Agrothereutes minousubae Nakanishi, 1965 in Expression of green fluorescent protein defines a specific population of lamina II excitatory interneurons in the GRP::eGFP mouse

Fig. 4. Agrothereutes minousubae Nakanishi, 1965 (A, B, KPM-NK 76585; C, D, KPM-NK 76590) and Aritranis kuro sp. nov. (E, G, I, KPM-NK 75824; F, H, KPM-NK 75825; J, K, KPM-NK 76577) ― A, C, E, J, lateral habitus; B, tegula and humeral plate (red arrow), dorso-lateral view; D, G, K, head, frontal view; F, dorsal habitus; H, propodeum, dorsal view; I, right wings.