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Figure S32 in Supplementary Materials for Precipitation is the main axis of tropical plant phylogenetic turnover across space and time
Figure S32 (lef). Internode certainty values based on the 821 single-copy gene trees mapped onto the single-copy genes ASTRAL species tree (Figure S14). For each node, the upper number shows the quartet-based Extended Quadripartition Internode Certainty (EQP-IC) score calculated with QuartetScores, and the lower number shows the bipartition-based Internode Certainty All score calculated with PhyParts, both rounded down to two digits. Boxes are coloured based on unrounded values: green for values ≥ 0.5, yellow for values ≥ 0 and <0.5, and red for values <0. Branch lengths are set equal for easier visualisation. Inset depicts a correlation plot between the two measures.
Figure S33 in Supplementary Materials for Precipitation is the main axis of tropical plant phylogenetic turnover across space and time
Figure S33 (right).'esults of ASTRAL's polytomy test based on the 821 single-copy gene trees mapped onto the single-copy genes ASTRAL species tree (Figure S14). Node numbers are tests of the null hypothesis that a branch should be replaced by a polytomy. Only node numbers> 0.05 are shown. Branch lengths are set equal for easier visualisation.
Figure S31 in Supplementary Materials for Precipitation is the main axis of tropical plant phylogenetic turnover across space and time
Figure S31. Gene tree incongruence mapped onto the time-calibrated version of the phylogenomic backbone of Caesalpinioideae. Each branch is coloured to reflect the ratio of total supporting versus total conflicting gene trees as determined by PhyParts. Clades named by Koenen et al. (24) are labelled. Two recent radiations in Madagascar, one in the Dichrostachys clade and one in Albizia, are highlighted.
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.
A transcontinental experiment elucidates (mal)adaptation of a cosmopolitan plant to climate in space and time
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Supplementary Materials - Integrating Spatial Analyses and Microbotanical Remains: A Methodological Approach for Investigating Plant Processing Activities and Domestic Spaces at Neolithic Çatalhöyük.
<p><strong>Supplementary Materials I, II, and III - Integrating Spatial Analyses and Microbotanical Remains: A Methodological Approach for Investigating Plant Processing Activities and Domestic Spaces at Neolithic Çatalhöyük.</strong></p> <p> </p> <p><strong>Supplementary I</strong> contains the raw data for the microbotanical analyses on Buildings 80 and 131. It consists of laboratory track sheets and the raw counts of phytoliths and starch grains from each building.</p> <p><strong>Supplementary II</strong> contains: a) A detailed description of all the starch grain typologies encountered in this research along with references from modern reference collections; b) A visual example of each of the phytolith morphotypes identified and c) The variogram and the Kriging error variances.</p> <p><strong>Supplementary III</strong> refers to the R code, building shape files (Mask), and spatial data utilized to produce the Kriging and IDW stipital objects.</p>
Data from: neglected puzzle pieces of urban green infrastructure: richness, cover, and composition of insect-pollinated plants in traffic-related green spaces
<p>Insect-pollinated vascular plants in spontaneous vegetation provide essential ecosystem services and benefit wildlife. However, floral communities associated with traffic-related green spaces are rarely considered valuable elements of urban green infrastructure (UGI). The dataset contains information on species-based floral communities of vascular insect-pollinated plants in traffic-related green spaces in three highly populated Finnish cities. Those are Helsinki (665 558 inhabitants), Tampere (244 029 inhabitants), and Turku (175 645 inhabitants). Data were collected during the mean flowering phenophase of vascular plants in July-August 2022 from two types of locations: (i) urban (city centers) and (ii) suburban (city outskirts), and from three types of traffic-related green spaces: (i) traffic islands, (ii) parking lots, (iii) road verges. The dataset contains information for the 93 vascular insect-pollinated plant species flowering during the survey. Sampling campaign was conducted in 90 sampling sites, and the dataset contains information on the location coordinates. In addition, the dataset possesses information on the amount of garbage pieces (cigarette filters, plastic boxes, or scraps) revealed for each sampling point in traffic-related green spaces.</p>
Weissflog et al. - Daytime but not plant trichomes provide herbivores with enemy-free space: of plasticine caterpillars, superheroes, and natural enemies.
<p>RAW DATA</p> <p>We use artificial prey to quantify spatial and temporal variation in predation pressure on insect herbivores in two tropical rainforest sites in Panama. </p> <p>We measured temporal and spatial variation in predation in the understory of the lowland rainforest of Barro Colorado Island in Panama [79°49.79´S, 9°9.48´E; 2600 mm yr<sup>-1</sup> rainfall, 3 mo dry season; = island experiment) in February 2020 by comparing diurnal and nocturnal attacks on plasticine caterpillars and hulks placed on glabrous and pubescent plants. We used model prey to measure predation pressure. Caterpillars (50 x 4 mm) and hulks (with a height of 30 mm that equaled the maximum height of the bent caterpillars) were molded from green, (to humans) odorless, non-toxic Newplast (Newclay Products Ltd., Newton Abbot, UK). Caterpillars were shaped and bent to mimic the posture of common geometrid caterpillars. Hulks resembled small (but fearsome) superhero figurines, known from the Marvel comics (Marvel Worldwide Inc.), and were shaped using custom made plastic molds. We used hulks as a control to the more naturally shaped caterpillar models to test whether objects resembling natural prey are indeed recognized as such by potential predators. All objects were modeled and handled using surgical gloves to avoid leaving unwanted cues (i.e., scent or other distracting contaminants) to predators. A hundred caterpillars and a hundred hulks were individually placed on 50 plants per host species. Objects were attached close to the midrib on the upper surface of plant leaves with a small amount of fast-setting glue, 30-80 cm from the ground. For four consecutive days (total of 96 h), the plasticine objects were inspected in 12 hour intervals at dusk and dawn (18:15 and 06:15 hours) to differentiate diurnal from nocturnal predation.</p> <p>Further, we conducted a follow-up experiment in a close-by mainland forest site in Gamboa, Parque Nacional Soberanía (79°43.38 S, 9°8.24 E; = mainland study) in early-March 2021. Following the same procedure as for the island experiment described above, we glued 27 caterpillars, 27 hulks, and 27 small caterpillars (30 x 2 mm) close to the midrib onto the upper side of leaves of tree saplings. In this follow-up experiment, we did however not select specific plant species, but chose saplings that were naturally growing in the field site. All plants were of similar size, with simple, elliptic to ovate, smooth-edged leaves, and without any foliar or stem pubescence. As before, all objects were carefully inspected for attack marks at 12 hour intervals at 18:15 and 06:15 hours for four consecutive days.</p>
Depicting the phenotypic space of the annual plant Diplotaxis acris in hyper-arid deserts
<p class="CxSpFirst">The phenotypic space encompasses the assemblage of trait combinations yielding well-suited integrated phenotypes. At the population level, understanding phenotypic space structure requires the quantification of among- and within-population variation in traits and the correlation pattern among them. Here, we studied the phenotypic space of the annual plant <i>Diplotaxis acris</i> occurring in hyper-arid deserts. Given the advance of warming and aridity in vast regions occupied by drylands, <i>D. acris</i> can indicate the successful evolutionary trajectory that many other annual plant species may follow in expanding drylands. To this end, we conducted a greenhouse experiment with 176 <i>D. acris</i> individuals from five Saudi populations to quantify the genetic component of variation in architectural and life-history traits. We found low among-population divergence but high among-individual variation in all traits. In addition, all traits showed a high degree of genetic determination in our study experimental conditions. We did not find significant effects of recruitment and fecundity on fitness. Finally, all architectural traits exhibited a strong correlation pattern among them, whereas for life-history traits, only higher seed germination implied earlier flowering. Seed weight appeared to be an important trait in <i>D. acris</i>, as individuals with heavier seeds tended to advance flowering and have a more vigorous branching pattern, which led to higher fecundity. Population divergence in <i>D. acris</i> might be constrained by the severity of the hyper-arid environment, but populations maintain high among-individual genetic variation in all traits. Furthermore, <i>D. acris</i> showed phenotypic integration for architectural traits and, to a lesser extent, for life-history traits. Overall, we hypothesize that <i>D. acris</i> may be fine-tuned to its demanding extreme environments. Evolutionary speaking, annual plants facing increasing warming, aridity and environmental seasonality might modify their phenotypic spaces towards new phenotypic configurations strongly dominated by correlated architectural traits enhancing fecundity and seed-related traits advancing flowering time.</p>
Data and R code used in Hennecke et al. "Plant species richness and the root economics space drive soil fungal communities"
<p>To investigate how plant diversity and root traits relate to soil fungal communities, in 2021 we collected trait data from plots in the Jena Experiment (https://the-jena-experiment.de; funded by the DFG FOR 5000) and characterized fungal communities by sequencing, respiration and lipid fatty acid quantification. </p>
Location and plant spacing affect biomass yield and nutritional value of pigeon pea forage
<p>An experiment was conducted to evaluate the effects of row spacing (RS) and interplant spacing (IPS) on the yield of total biomass, leaf, and edible twigs, and nutritive value of pigeon pea,(<i>Cajanus cajan </i>L. Millsp.<i>)</i> at three locations in the Rift Valley area of Ethiopia, using a randomized completed block design with three replications in 3 × 3 factorial arrangement; three RS ( 25, 50, and 75 cm) and three IPS (15, 30, and 45 cm). Row spacing × IPS of 25 × 30 cm and 50 × 15 cm, gave greater total biomass yield than the other RS and IPS combinations. A similar result was found for edible plant yield. At Hawassa the greater leaf CP was found for the wider IPS of 45 cm than the narrower IPS of 30 cm (318 Vs. 303 g kg<sup>-1 </sup>). At Wondo-Genet, the greater leaf in vitro digestible organic matter (597 Vs. 582 g kg<sup>-1 </sup>) was found for the wider RS of 75 cm than narrower RS. Similar to leaf, a better nutritional value of edible twigs was found for a wider RS and IPS than narrower RS and IPS at Hawassa and Wondo-Genet. In contrast at Aliyu-Amab, a better nutritional value of edible twigs was found for narrower RS and IPS than the wider spacing. Thus, it can be concluded that RS × IPS of 25 × 30 cm or 50 × 15 cm are advisable for pigeon pea forage production.</p>
Space resource utilization of dominant species integrates abundance- and functional-based processes for better predictions of plant diversity dynamics
<p>Sustainable ecosystem management relies on our ability to predict changes in plant diversity and to understand the underlying mechanisms. Empirical evidence demonstrates that abundance- and functional-based processes simultaneously explain the loss of plant diversity in response to human activities. Recently, a novel indicator based on percent cover (CoverD) and maximum height (HeightD) of the dominant plant species – Space Resource Utilization (SRUD) – has proven to give robust and better predictions of plant diversity dynamics than community biomass. Whether the superior predictive ability of SRUD is due to its capacity to simultaneously capture abundance- and functional-based processes remains unknown. Here, we tested this hypothesis by quantifying mechanistic links between changes in SRUD and biodiversity in response to nutrients and herbivores. Furthermore, we assessed the relative contribution of dominant, intermediate, and rare species to reduced density of individuals by combining null model analysis with field experiments. We found that SRUD successfully captured changes in ground-level light availability and changes in the number of individuals to predict plant diversity dynamics, and each of CoverD and HeightD partly and independently contributed to both processes. Comparative results from null model analysis and field experiments confirmed that individual losses of dominant, intermediate, and rare species followed non-random processes. Specifically, compared with random loss process, rare species lost proportionally more individuals and thus disproportionately contributed to species loss, while dominant and intermediate species lost less. Our results demonstrate that SRUD captures both abundance- and functional-based processes thus explaining why SRUD provides more accurate predictions of changes in species diversity. Given that rare species can play an important role in shaping community structure, resisting against invasion, impacting higher trophic levels, and providing multiple ecosystem functions, reducing the SRU of dominant species could alleviate the risk of exclusion of rare species by mitigating abundance- and functional-based competition processes.</p>
Resampling alpine herbarium records reveals changes in plant traits over space and time - dataset
<p><strong>Data overview:</strong></p> <p>These data correspond to the analyses conducted for the article "Resampling alpine herbarium records reveals changes in plant traits over space and time" by Francesca Jaroszynska, Christian Rixen, Sarah Woodin, Jonathan Lenoir and Sonja Wipf, in Journal of Ecology</p> <p><strong>Metadata for jaroszynska_herbarium_traits_data.csv:</strong></p> <p>date = date; date of collection</p> <p>time = factor; time of collection (historical or recent)</p> <p>elevation = numerical; elevation in metres above sea level of the sample collection site</p> <p>selevation = numerical; scaled <em>elevation</em></p> <p>selevation2 = numerical; elevation in metres above sea level of sample collection site (elevation/1000).</p> <p>sSlope = numerical; scaled slope (slope/10)</p> <p>slope = numerical; computed slope based on elevation</p> <p>trait = string; name of the measured trait</p> <ul> <li> <p>crFlowerN = numerical; Cardamine resedifolia; number of flowers</p> </li> <li> <p>crHeight = numerical; Cardamine resedifolia; plant height</p> </li> <li> <p>crLeafL = numerical; Cardamine resedifolia; length of longest leaf</p> </li> <li> <p>crRosetteLeafN = numerical; Cardamine resedifolia; number of leaves in rosette</p> </li> <li> <p>paBasalLeafL = numerical; Poa alpina; basal leaf length</p> </li> <li> <p>paInflorescenceL = numerical; Poa alpina; inflorescence length</p> </li> <li> <p>paHeight = numerical; Poa alpina; plant height</p> </li> <li> <p>pvInfL = numerical; Polygonum viviparum; length of inflorescence</p> </li> <li> <p>pvLA = numerical; Polygonum viviparum; leaf area (length x width)</p> </li> <li> <p>pvLeafL = numerical; Polygonum viviparum; leaf length</p> </li> <li> <p>pvRepH= numerical; Polygonum viviparum; plant height</p> </li> <li> <p>rgFlowerStemL = numerical; Ranunculus glacialis; flowering stem length</p> </li> <li> <p>rgLeafStemL = numerical; Ranunculus glacialis; petiole length</p> </li> <li> <p>rgLeafW = numerical; Ranunculus glacialis; leaf width</p> </li> <li> <p>rgFlowerN = integer; Ranunculus glacialis; number of flowers</p> </li> </ul> <p> </p> <p>traitGroup = factor; the group to which each trait belongs (VegHeight = vegetative height, ReprHeight = reproductive height, ReprOut = reproductive output, PhotoCap = photosynthetic capacity)</p> <p>value = numerical; value of the trait measured</p> <p>species = factor; species code (car_res = Cardamine resedifolia, ran_glac = Ranunculus glacialis, pol_viv = Polygonum viviparum, poa_alp = Poa alpina)</p> <p>transect = string; transect along which the herbarium sample was taken</p> <p>confidence = factor; reliability of the metadata associated with the herbarium sample, assigned by the authors Jaroszynska and Wipf (low, medium, high)</p> <p>northness = numerical; northness</p> <p>eastness = numerical; eastness</p> <p>observer = string; botanist who conducted the collection</p> <p>sheet = string; unique identifier for the collection sheet</p> <p> </p> <p><strong>Metadata for jaroszynska_climate_traits_data.csv:</strong></p> <p>year = year; year of sample collection</p> <p>Month = integer; month of sample colection</p> <p>Temperature = numerical; monthly average temperature (ºC)</p> <p>Precipitation = numerical; monthly total precipitation (mm)</p> <p>yearMonth = string; year.month</p> <p>season = factor; season associated to the corresponding month (spring, summer, autumn, winter)</p> <p>timePeriod = factor; climate period referring to the time before, after, or during the baseline reference period (see article for further details)</p> <p>meanAnnTemp = numerical; mean annual temperature (ºC)</p> <p>sumAnnPrecip = numerical; total annual precipitation (mm)</p> <p>meanSeaTemp = numerical; mean seasonal temperature (ªC)</p> <p>sumSeaPrecip = numerical; total seasonal precipitation (mm)</p> <p>meanRefTemp = numerical; mean seasonal temperature for reference period (ªC)</p> <p>temp_anomaly = numerical; temerature anomaly from the reference period (ªC)</p> <p>lagMonths = string; used in seasonal calculation</p> <p>seasonal_precip = numerical; seasonal precipitation (mm)</p> <p>precip_anomaly = numerical; seasonal precipitation anomaly (mm)</p>
Figure S51 in Supplementary Materials for Precipitation is the main axis of tropical plant phylogenetic turnover across space and time
Figure S51. Higher resolution version of Figure 1. See Figure 1 for caption.
Figure S12 in Supplementary Materials for Precipitation is the main axis of tropical plant phylogenetic turnover across space and time
Figure S12 (lef). Numbers of taxa per alignment.
Figure S8 in Supplementary Materials for Precipitation is the main axis of tropical plant phylogenetic turnover across space and time
Figure S8 (lef). Percentages of reads on target per sample.
Figure S7 in Supplementary Materials for Precipitation is the main axis of tropical plant phylogenetic turnover across space and time
Figure S7 (lef). Fractions of filtered reads per sample.
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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)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
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.