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Figure S36 in Supplementary Materials for Precipitation is the main axis of tropical plant phylogenetic turnover across space and time

Figure S36. Phyloregionalization of North America using the metachronogram. Subfigures show clustering results with two to eight phyloregions, as well as the results of phyloregionalization analyses using the geographic residuals of phylogenetic turnover, and ancient phylogenetic turnover with a cut-off of 5, 10, and 20 million years.

Figure S14 in Supplementary Materials for Precipitation is the main axis of tropical plant phylogenetic turnover across space and time

Figure S14. Phylogeny of Caesalpinioideae. ASTRAL species tree based on the 821 single-copy gene trees. Local posterior probability support values are only shown for nodes with a local posterior probability <1. Branch lengths are expressed in coalescent units. Terminal branches were assigned an arbitrary uniform length for visual clarity.

Fig. 2 in Precipitation is the main axis of tropical plant phylogenetic turnover across space and time

Fig. 2. Drivers of phylogenetic turnover of Mimosoid legumes across the global lowland tropics. Bars show relative fractions of phylogenetic turnover explained by predictors (rescaled to add up to one). Numbers above bars are absolute explained percentages of turnover (tables S12 and S20). (A) Phylogenetic turnover explained by climatic distance (maroon), geographic distance (blue), or their interaction (cream). Turnover is assessed across four depths in the phylogeny: with the full metachronogram (age cutoff of 0) and with all clades younger than 5, 10, and 20 Ma collapsed. Note that it was not possible to fit a model to the phylogeny collapsed at 20 Ma for the pantropical and Australian models. (B) Phylogenetic turnover explained by MAP (green) and/or annual mean temperature (orange). Turnover is expressed as phylogenetic turnover not explained by geographic distance ("geographic residuals"). (C) Geographic residuals of phylogenetic turnover explained by MAP (green) and/or precipitation seasonality (gray; left) or dry season length (DSL) (i.e., the number of consecutive months with precipitation <100 mm/month; yellow; right). See fig. S45 for results obtained with an alternative, genus-level Mimosoid phylogeny. P, MAP; T, annual mean temperature; Pseas, precipitation seasonality.

Fig. 1 in Precipitation is the main axis of tropical plant phylogenetic turnover across space and time

Fig. 1. Mimosoid evolution and diversity across precipitation gradients. (A) Phylogeny of Mimosoid legumes showing the evolution of precipitation niches and transcontinental dispersal events through time. Branch colors correspond to mean annual precipitation (MAP) estimates [see (C) for scale]. Pie charts at tips and nodes of named clades [sensu (24)] represent observed and estimated spatial distributions [based on area definitions in (D)]. Ancestral niches and areas were estimated using a complete metachronogram for Caesalpinioideae, including non-Mimosoid Caesalpinioideae taxa, but only the Mimosoid clade is shown here. Green circles on branches indicate shifts between precipitation categories [following (17)] that encompass a difference of at least 250-mm MAP; red triangles indicate postulated transcontinental dispersals according to the best-supported model. The six most species-rich genera are labeled. (B) Fractions of niche shifts and transcontinental dispersal events, averaged across multiple optimizations, relative to total phylogenetic splits plotted through time for 5-Ma bins. (E) Mimosoid growth form diversity across the tropical precipitation gradient, from deserts with <50-mm MAP (left) through savannas to rain forests with>5000-mm MAP (right). See the Supplementary Results for species names and photographers. See fig. S51 for more information.

opencc-zeroMay 2023View details →

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Barcode database of scleractinian corals (Cnidaria, Scleractinia) in two tropical marine protected areas (Perhentian and Redang Islands Marine Parks, Malaysia)

Coral reefs are particularly vulnerable to climate change as an elevation of sea surface temperatures would cause most coral species to bleach rapidly by expulsing their algal symbionts Symbiodiniaceae. Loss of these symbionts in coral species during stress-related "bleaching" events can lead to mass mortality of these corals and the collapse of the associated reef ecosystem. This makes the inventory of the diversity of coral species and their symbionts, in particular, genetic diversity, critical to the understanding of the assessment of coral reef resilience and ecosystem sustainability. The use of DNA barcoding has been proven useful in biodiversity assessments and monitoring schemes of marine ecosystems, particularly the highly diverse coral reef ecosystems. However, this information is lacking in Malaysia. Therefore, a comprehensive reference database of genetic sequences that represent the present coral reef biodiversity in the country would be needed. Here, we barcoded the scleractinian coral samples, the reef-building corals, collected from the two marine protected areas of Malaysia, Perhentian and Redang Islands Marine Parks, using the cytochrome c oxidase I (COI) marker. The data released from this project represent the first comprehensive reference database of the scleractinian coral diversity on the northeastern coasts of Malaysian coral reefs. A total of 62 nucleotide COI sequences of scleractinian corals collected from the two marine parks were obtained. Of these, a total of 34 taxa belonging to nine families were included in the dataset. This dataset added information on the occurrence and distribution of scleractinian coral species from the two tropical marine protected areas in the South China Sea (Western Pacific).

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Data from: Landscape variation in defense traits along gradients of multiple resources in a tropical savanna plant

Many plant species are widely distributed and consequently are exposed to multiple abiotic factors and diverse herbivores, each of which may distinctly affect the magnitude of different defense traits. Alternative theories for optimal allocation to plant defense traits predict both positive and negative associations between magnitude of defense and resource availability. These predictions may apply even within species. This suggests potential for a single species' patterns of association of defense traits and resources to vary with both the type of defense and identity of resource, but relatively few studies have explored intraspecific variation in multiple defense traits along several resource gradients simultaneously. In order to address this gap, especially in an ecosystem dominated by large mammalian herbivores, we assessed relationships between multiple resources (rainfall, soil N, and soil P) and plant defense traits (prickle density, phenolics, and lignin content) using a widely distributed tropical savanna herb, Solanum incanum, growing in naturally occurring resource gradients within the Serengeti National Park. We found substantial intraspecific variation in all three defense traits across sites (n =43). Variation in prickle density was positively associated with rainfall and soil P, but not soil N. In contrast to prickle density, phenolics and lignin were uncorrelated with all three resource gradients. This independent association of soil P with a carbon-based defense, prickle density, suggests potential for resources that are not components of defenses to influence allocation to defense traits. Such influence may reflect association between resource and herbivore abundance and/or preference. These varied patterns in resource-defense associations further emphasize the tremendous variation in anti-herbivore traits which may be influenced by different plant resources and highlight the need to consider multiple resource gradients in understanding evolution of plant traits.

opencc-zeroMay 2023View details →

Figures 12–17 in A New Subspecies of Philiris diana Waterhouse & Lyell, 1914 (Lepidoptera: Lycaenidae) from the Wet Tropics of Northern Australia

Figures 12–17. Adults of Philiris diana fortuna ssp. nov.: (12, 13) holotype male, showing dorsal and ventral views (ANIC); (14) paratype male, dorsal view showing variation (BHC); (15, 16) paratype female, showing dorsal and ventral views (ANIC); (17) paratype female, dorsal view showing variation (BHC). Scale bar = 10 mm.

Figures 18–29 in A New Subspecies of Philiris diana Waterhouse & Lyell, 1914 (Lepidoptera: Lycaenidae) from the Wet Tropics of Northern Australia

Figures 18–29. Male genitalia of the Philiris diana species-group in Australia and Papua New Guinea: (18–20) P. diana fortuna ssp. nov. showing posterior, left lateral and right lateral views; (21–23) P. diana diana showing posterior, left lateral and right lateral views; (24–26) P. papuanus kerri showing posterior, left lateral and right lateral views; (27–29) P. papuanus papuanus showing posterior, left lateral and right lateral views. Scale bar = 1 mm.

Figures 4–11 in A New Subspecies of Philiris diana Waterhouse & Lyell, 1914 (Lepidoptera: Lycaenidae) from the Wet Tropics of Northern Australia

Figures 4–11. Adults of Philiris diana diana: (4, 5, 7) lectotype male in AMS, showing dorsal and ventral views and label data; (8, 9, 11) paralectotype female in AMS, showing dorsal and ventral views and label data; (6) male, dorsal view, reared from larva from near Kuranda (BHC); (10) female, dorsal view, reared from larva from near Cairns (EPC). Scale bar = 10 mm.

Figure 3 in A New Subspecies of Philiris diana Waterhouse & Lyell, 1914 (Lepidoptera: Lycaenidae) from the Wet Tropics of Northern Australia

Figure 3. Neighbor Joining phylogenetic tree of Philiris diana based on mitochondrial cytochrome c oxidase subunit I (658 bp "barcode" region). Outgroups are not shown. Scale bar represents number of substitutions/site.

Figures 30–37 in A New Subspecies of Philiris diana Waterhouse & Lyell, 1914 (Lepidoptera: Lycaenidae) from the Wet Tropics of Northern Australia

Figures 30–37. Life history and habitus of Philiris diana fortuna ssp. nov.: (30, 31) larva instar III, showing examples of variation in colour pattern, with lightly-marked and heavily-marked forms; (32, 33) larva instar VI, showing examples of variation in colour pattern, with yellow-brown striped and red-brown striped forms; (34, 35) pupa, showing examples of variation in colour pattern, with lightly marked and heavily marked forms; (36) adult female, newly-emerged at rest on foliage of Litsea leefeana; (37) adult pair in copula, with male on left and female on right.

Figures 1–2 in A New Subspecies of Philiris diana Waterhouse & Lyell, 1914 (Lepidoptera: Lycaenidae) from the Wet Tropics of Northern Australia

Figures 1–2. Right fore- and hindwings of Philiris diana diana, showing measurements of quantitative characters for traits 1–5 (see Materials and Methods): (1) female; (2) male. Letters denote the following characters: A = area of forewing; B = area of forewing white central patch; C = curved area of forewing enclosed between termen and line between end of veins R5 and 1A+2A; D = width of black terminal band of forewing at vein M3; E = length of forewing from base to end of vein M3; F = width of black terminal band of hindwing at vein M3; G = length of hindwing from base to end of vein M3.

opencc-by-4.0Dec 2022View details →

Dataset for Modelling the long-term carbon storage potential from recalcitrant matter inputs in tropical arable croplands

Soil organic carbon dynamics for Ecuadorian croplands on the period 2020-2070, under RCP4.5, for various carbon sequestration alternatives, relative to a business as usual situation. Results obtained from adapted version of RothC for carbon sequestration technologies.

opencc-by-4.0Jun 2023View details →

Tropical dry woodland loss occurs disproportionately in areas of highest conservation value

This data repository contains the data results used to analyse how deforestation dynamics relate to areas of woodland protection and to conservation priorities across the world's tropical dry woodlands. We do this for the period of 2000 to 2020, at 10-km spatial resolution (Coordinate System: WGS_1984_Mollweide, float format) following Buchadas et al. (2022) methods available  in https://doi.org/10.1038/s41893-022-00886-9. Datasets used for this analysis are generally publicly available, forest cover and loss data are available at: https://data.globalforestwatch.org/. The data on protected areas is available at https://www.protectedplanet.net/. The global conservation priority layers have been made openly available as part of Jung et al. (2021) at https://doi.org/10.5281/zenodo.5006332. The map of Indigenous Peoples' Lands can be obtained from the authors on reasonable request (Garnett et al. 2018). The data are not publicly available due to privacy or ethical restrictions. Thus we refrain from sharing the primary data that includes it, here. Further details of the datasets can be found in Buchadas et. al. (2023) For further questions or issues with the datasets, please contact Ana Buchadas at ana.buchadas@geo.hu-berlin.de.

opencc-by-4.0Oct 2023View details →

Dominant nitrogen metabolisms of a tropical monomictic lake identified using genome resolved metatranscriptomics

Dataset for manuscript entitled "Dominant nitrogen metabolisms of a tropical monomictic lake identified using genome resolved metatranscriptomics" Abstract: Reactive nitrogen (N) is one of the principal drivers of primary productivity across aquatic ecosystems. However, N cycling in tropical inland waters is less well understood relative to temperate ecosystems. In particular, N cycling in monomictic tropical lakes may be distinct from that of their temperate counterparts due to the warm (>20 °C) temperatures of their anoxic hypolimnions (deepest portion of a stratified water column). These warm anoxic conditions have the potential to dramatically alter ecosystem biogeochemistry by releasing anaerobic metabolic pathways from temperature limitations that are present in anoxic strata of lake ecosystems at higher latitudes. To explore dominant N transformation pathways under warm anoxic conditions, we combined measurements of geochemistry and water column thermophysical structure with genome resolved metatranscriptomic analyses of the water column microbiome in Lake Yojoa, Honduras. Lake Yojoa is characterized by a pronounced accumulation of ammonium in the hypolimnions during stratification, beginning in May, and an annual mixing event that releases reactive N to the photic zone, in November. We sampled Lake Yojoa both above (1m) and below (16m) the thermocline at three locations, in June 2021, when the water column was stratified, and again at the same depths and locations in January 2022, when the water column was mixed. We identified 335 different lineages and significantly different microbiome membership between seasons and, in June, between depths. Expression of genes associated with N metabolic pathways was highest in June below the thermocline. In particular, we found nrfA, along with other respiratory NO2- reductases, was upregulated relative to other N metabolism genes in the June hypolimnions, suggesting that dissimilatory nitrate reduction to ammonium (DNRA) was partially responsible for the previously observed ammonium accumulation. DNRA is infrequently identified as a dominant N transformation pathway in natural aquatic ecosystems. This work highlights the need to better define N biogeochemistry in the poorly understood warm anoxic hypolimnions of tropical lake ecosystems. Defining the dominant anaerobic pathways when temperature is not limiting is one key to providing a more complete understanding of how rapidly occurring global change is altering tropical inland waters. Files described below:  Yojoa_572_MAGs_scaffolds.fna.zip scaffolds for 572 dereplicated MAGs  Yojoa_572_MAGs_annotations.tsv.zip DRAM annotations for 572 dereplicated MAGs  bipartitionsBranchLabels.nxr_nar_for_tree_aligned.fasta_mode_low.renamed  phylogenetic tree for nar/nxr genes bipartitionsBranchLabels.amo_pmo_fortree_aligned.fasta_mode_low.renamed phylogenetic tree for amo/pmo genes YojoaMasterSheet_final.xls MAG inventory with accession numbers and quality stats MetaT_norm.counts.rpk_edger.bins_mean.csv mean geTMM values per bin expressed in metatranscriptomic data MetaT_geTMM_norm.counts.rpk_edger_genes.csv geTMM values per gene expressed in metranscriptomic data geochem_data_Yojoa.csv geochemical data from each location including NH4+, NO3-, total phosphorus (TP), and dissolved organic carbon (DOC) profile_data_Yojoa.csv depth profile data at each location including temperature and dissolved oxygen measurements

opencc-by-4.0Jun 2023View details →

Spreadsheets to model counterfactual tropical forest losses (1990-2019) for Brazil, Democratic Republic of Congo and Indonesia

18 spreadsheets used to simulate the counterfactual forest losses underlying the publication: "Trends in tropical forest loss and the social value of emission reductions" by Thomas Knoke, Nick Hanley, Rosa Maria Roman-Cuesta, Ben Groom, Frank Venmans and Carola Paul published in Nature Sustainability (DOI: 10.1038/s41893-023-01175-9) Each spreadsheet covers a period of five years. The dynamic robust multifunctional land-use allocation model is based on Knoke, T. et al. Accounting for multiple ecosystem services in a simulation of land-use decisions: Does it reduce tropical deforestation? Glob. Chang. Biol. 26, 2403–2420; 10.1111/gcb.15003 (2020). For details see linked publication and README file

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Seed dispersal syndrome predicts ethanol concentration of fruits in a tropical dry forest

Studying fruit traits and their interactions with seed dispersers can improve how we interpret patterns of biodiversity, ecosystem function, and evolution. Mounting evidence suggests that fruit ethanol is common, variable, and may exert selective pressures on seed dispersers. To test this, we comprehensively assess fruit ethanol content in a wild ecosystem and explore sources of variation. We hypothesise that both phylogeny and seed dispersal syndrome explain variation in ethanol levels, and we predict that fruits with mammalian dispersal traits will contain higher levels of ethanol than those with bird dispersal traits. We measured ripe fruit ethanol content in species with mammal- (n = 16), bird- (n = 14), or mixed-dispersal (n = 7) syndromes in a Costa Rican tropical dry forest. Seventy-eight percent of fruit species yielded measurable ethanol concentrations. We detected a phylogenetic signal in maximum ethanol levels (Pagel's λ = 0.82). Controlling for phylogeny, we observed greater ethanol concentrations in mammal-dispersed fruits, indicating that dispersal syndrome helps explain variation in ethanol content and that mammals may be more exposed to ethanol in their diets than birds. Our findings further our understanding of wild fruit ethanol and its potential role as a selective pressure on frugivore sensory systems and metabolism.

opencc-zeroJun 2023View details →

opencc-by-4.0Feb 2021View details →

Figure 2 in Revisiting the ideas of trees as templates and the competition paradigm in pairwise analyses of ground-dwelling ant species occurrences in a tropical forest

Figure 2 Presence probabilities of ant species in each tree species only for those one involved in nonrandom pairs for the two sampling techniques and seasons. PR: pitfall,rainy season; PD: pitfall, dry season; WR: Winkler, rainy season; WD: Winkler, dry season. GG= Guarea guidonia; IE= Inga edulis; NM= Nectandra membranacea; PG= Piptadenia gonoachanta.

Replication code and data for "Comparing measured dietary variation within and between tropical hunter-gatherer groups to the Paleo Diet".

Replication code and data for the publication "Comparing measured dietary variation within and between tropical hunter-gatherer groups to the Paleo Diet" appearing in the American Journal of Clinical Nutritian. Files include: 1) R replication code (5 files that should be run sequentially in numerical order). 2) "raw_diet_data.csv": 1 file that is ingested by the R code and used for the main analyses. 3) "lipid_classes.csv" and "nutriants_by_source_raw.csv": 2 files that are ingested by the R code and used for the supplementary analyses. 4) "wc2.1_30s_bio_1.tif": 1 file of GeoTiff data on annual mean temperature from the WorldClim v. 2.1 dataset (http://www.worldclim.com/version2) that is ingested by the R code. 5) "table_1.csv": a cleaned version of the data used in analyses that is output by the R code. 6) "table_hg_diet_data.csv" and "table_seasonal_diet_data.csv": 2 files containing seasonal and HG diet data that were not used in analyses, which are output by the R code.