Find research datasets worth reusing

Data from: A topoclimate model for Quaternary insular speciation

Aim. Understanding speciation as a process on islands, particularly speciation within individual islands, is key to explain the high levels of invertebrate speciation that characterise many oceanic islands and archipelagos. Here we propose an insular topoclimate model for Quaternary diversification (ITQD), and test the general prediction that, within a radially eroded conical island, within glacial climate conditions facilitate the divergence of populations within species across valleys. Location. Gran Canaria, Canary Islands. Taxon. The Laparocerus tessellatus beetle species complex (Coleoptera, Curculionidae). Methods. We characterise individual-level genomic relationships using single nucleotide polymorphisms produced by double-digest restriction site associated DNA sequencing (ddRAD-seq). A range of parameter values were explored in order to filter our data. We assess individual relatedness, species boundaries, demographic history and spatial patterns of connectivity. Results. The total number of ddRAD-seq loci per sample ranges from 4576 to 512, with 11.12% and 4.84% of missing data respectively, depending on the filtering parameter combination. We consistently infer four genetically distinct ancestral populations and two presumed cases of admixture, one of which is largely restricted to high altitudes. Bayes factor delimitation support the hypothesis of four species, which is consistent with the four inferred ancestral gene pools. Landscape resistance analyses identified genomic relatedness among individuals in two out of the four inferred species to be best explained by annual precipitation during the last glacial maximum rather than geographic distance. Main conclusions. Our data reveal a complex speciation history involving population isolation and admixture, with broad support for the ITQD model here proposed. We suggest that further studies are needed to test the generality of our model, and enrich our understanding of the evolutionary process in island invertebrates. Our results demonstrate the power of ddRAD-seq data to provide a detailed understanding of the temporal and spatial dynamics of insular biodiversity.

Data from: Testing models of reciprocal relations between social influence and integration in STEM across the college years

The present study tests predictions from the Tripartite Integration Model of Social Influences (TIMSI) concerning processes linking social interactions to social integration into science, technology, engineering, and mathematics (STEM) communities and careers. Students from historically overrepresented groups in STEM were followed from their senior year of high school through their senior year in college. Based on TIMSI, we hypothesized that interactions with social influence agents (operationalized as mentor network diversity, faculty mentor support, and research experiences) would promote both short- and long-term integration into STEM via social influence processes (operationalized as science self-efficacy, identity, and internalized community values). Moreover, we examined the previously untested hypothesis of reciprocal influences from early levels of social integration in STEM to future engagement with social influence agents. Results of a series of longitudinal structural equation model-based mediation analyses indicate that, in the short term, higher levels of faculty mentorship support and research engagement, and to a lesser degree more diverse mentor networks in college promote deeper integration into the STEM community through the development of science identity and science community values. Moreover, results indicate that, in the long term, earlier high levels of integration in STEM indirectly influences research engagement through the development of higher science identity. These results extend our understanding of the TIMSI framework and advance our understanding of the reciprocal nature of social influences that draw students into STEM careers.

opencc-zeroSep 2019View details →

Data from: Aphid cards – useful model for assessing predation rates or bias prone nonsense?

Predation on pest organisms is an essential ecosystem function supporting yields in modern agriculture. However, assessing predation rates is intricate and they can rarely be linked directly to predator densities or functions. We tested whether sentinel prey aphid cards are useful tools to assess predation rates in the field. Therefore, we looked at aphid cards of different sizes on the ground level as well as within the vegetation. Additionally, by trapping ground dwelling predators, we examined whether obtained predation rates could be linked to predator densities and traits. Predation rates recorded with aphid cards were independent of aphid card size. However, predation rates on the ground level were three times higher than within the vegetation. We found both predatory carabid activity densities as well as community weighted mean body size to be good predictors for predation rates. Predation rates obtained from aphid cards are stable over card type and related to predator assemblages. Aphid cards therefore are a useful, efficient method for rapidly assessing the ecosystem function predation. Their use might especially be recommended for assessments on the ground level and when time and resource limitations rule out more elaborate sentinel prey methods using exclosures with living prey animals.

opencc-by-4.0Aug 2020View details →

Data and scripts for 'Potential and limitations of machine learning for modeling warm-rain cloud microphysical processes'

Data and scripts for "Potential and limitations of machine learning for modeling warm-rain cloud microphysical processes" by Axel Seifert and Stephan Rasp, J. Adv. Modeling Earth Systems, 12, 2020, https://doi.org/10.1029/2020MS002301

opencc-by-4.0Aug 2020View details →

Symmetric instability model data

The data include CROCO output netcdf file  for symmetric instability analysis and also the grid, initial and forcing files to set up the CROCO simulations. The configuration files are available at https://github.com/jhdong2016/SI_model. Files with names including "si" are from SI550, otherwise from KPP550.

Data from: Exploring a Pool-seq only approach for gaining population genomic insights in non-model species

Developing genomic insights is challenging in non-model species for which resources are often scarce and prohibitively costly. Here, we explore the potential of a recently established approach using Pool-seq data to generate a de novo genome assembly for mining exons, upon which Pool-seq data is used to estimate population divergence and diversity. We do this for two pairs of sympatric populations of brown trout (Salmo trutta); one naturally sympatric set of populations and another pair of populations introduced to a common environment. We validate our approach by comparing the results to those from markers previously used to describe the populations (allozymes and individual based SNPs) and from mapping the Pool-seq data to a reference genome of the closely related Atlantic salmon (Salmo salar). We find that genomic differentiation (FST) between the two introduced populations exceeds that of the naturally sympatric populations (FST = 0.13 and 0.03 between the introduced and the naturally sympatric populations, respectively), in concordance with estimates from the previously used SNPs. The same level of population divergence is found for the two genome assemblies but estimates of average genic diversity differ (π ≈0.002 and π ≈0.001 when mapping to S. trutta and S. salar, respectively), although the relationships between population values are largely consistent. This discrepancy might be attributed to biases when mapping to a haploid condensed assembly made of highly fragmented read data compared to using a high-quality reference assembly from a divergent species. We conclude that the Pool-seq only approach can be suitable for detecting and quantifying genome wide population differentiation, and for comparing genomic diversity in populations of non-model species where reference genomes are lacking.

Data from: Development of an adrenocortical cancer humanized mouse model to characterize anti-PD1 effects on tumor microenvironment

Context: While the development of immune checkpoint inhibitors has transformed treatment strategies of several human malignancies, research models to study immunotherapy in ACC are lacking. Objective: To explore the effect of anti-PD1 immunotherapy on the alteration of the immune milieu in ACC in a newly generated preclinical model and correlate with the response of the matched patient. Design, Setting and Intervention: To characterize the CU-ACC2-M2B patient-derived xenograft in a humanized mouse model, evaluate the effect of a PD-1 inhibitor therapy and compare to the CU-ACC2 patient with metastatic disease. Results: Characterization of the CU-ACC2-hu-CB-BRGS model confirmed ACC origin and match with the original human tumor. Treatment of the mice with pembrolizumab demonstrated significant tumor growth inhibition (TGI = 60%) compared to controls, which correlated with increased tumor infiltrating lymphocyte activity, with an increase of human CD8+ T cells (p<0.05), HLA-DR+ T cells (p<0.05) as well as Granzyme B+ CD8+ T cells (<0.001). In parallel, treatment of the CU-ACC2 patient, who had progressive disease, demonstrated a partial response with 79%-100% reduction in the size of target lesions, and no new sites of metastasis. Pre-treatment analysis of the patient's metastatic liver lesion demonstrated abundant intra-tumoral CD8+ T cells by immunohistochemistry. Conclusions: Our study reports the first humanized ACC PDX mouse model which may be useful to define mechanisms and biomarkers of response and resistance to immune-based therapies, to ultimately provide more personalized care for patients with ACC.

Data from: Evaluating presence-only species distribution models with discrimination accuracy is uninformative for many applications

Aim: Species distribution models are used across evolution, ecology, conservation, and epidemiology to make critical decisions and study biological phenomena, often in cases where experimental approaches are intractable. Choices regarding optimal models, methods, and data are typically made based on discrimination accuracy: a model's ability to predict subsets of species occurrence data that were withheld during model construction. However, empirical applications of these models often involve making biological inferences based on continuous estimates of relative habitat suitability as a function of environmental predictor variables. We term the reliability of these biological inferences "functional accuracy." We explore the link between discrimination accuracy and functional accuracy. Methods: Using a simulation approach we investigate whether models that make good predictions of species distributions correctly infer the underlying relationship between environmental predictors and the suitability of habitat. Results: We demonstrate that discrimination accuracy is only informative when models are simple and similar in structure to the true niche, or when data partitioning is geographically structured. However, the utility of discrimination accuracy for selecting models with high functional accuracy was low in all cases. Main conclusions: These results suggest that many empirical studies and decisions are based on criteria that are unrelated to models' usefulness for their intended purpose. We argue that empirical modeling studies need to place significantly more emphasis on biological insight into the plausibility of models, and that the current approach of maximizing discrimination accuracy at the expense of other considerations is detrimental to both the empirical and methodological literature in this active field. Finally, we argue that future development of the field must include an increased emphasis on simulation; methodological studies based on ability to predict withheld occurrence data may be largely uninformative about best practices for applications where interpretation of models relies on estimating ecological processes, and will unduly penalize more biologically informative modeling approaches.

Model output data for "Negative density-dependent dispersal emerges from the joint evolution of density- and body condition-dependent dispersal strategies"

Empirical studies have documented both positive and negative density-dependent dispersal, yet most theoretical models predict positive density dependence as a mechanism to avoid competition. Several hypotheses have been proposed to explain the occurrence of negative density-dependent dispersal, but few of these have been formally modeled. Here, we developed an individual based model of the evolution of density-dependent dispersal. This model is novel in that it considers the effects of density on dispersal directly, and indirectly through effects on individual condition. Body condition is determined mechanistically, by having juveniles compete for resources in their natal patch. We found that the evolved dispersal strategy was a steep, increasing function of both density and condition. Interestingly, although populations evolved a positive density-dependent dispersal strategy, the simulated metapopulations exhibited negative density-dependent dispersal. This occurred because of the negative relationship between density and body condition: high density sites produced low condition individuals that lacked the resources required for dispersal. Our model therefore generates the novel hypothesis that observed negative density-dependent dispersal can occur when high density limits the ability of organisms to disperse. We suggest that future studies consider how phenotype is linked to the environment when investigating the evolution of dispersal.

Genetic data improves niche model discrimination and alters the direction and magnitude of climate change forecasts

Ecological niche models (ENMs) have classically operated under the simplifying assumptions that there are no barriers to gene flow, species are genetically homogeneous (i.e., no population-specific local adaptation), and all individuals share the same niche. Yet, these assumptions are violated for most broadly distributed species. Here we incorporate genetic data from the widespread riparian tree species narrowleaf cottonwood (Populus angustifolia) to examine whether including intraspecific genetic variation can alter model performance and predictions of climate change impacts. We found that (1) P. angustifolia is differentiated into six genetic groups across its range from México to Canada, and (2) different populations occupy distinct climate niches representing unique ecotypes. Comparing model discriminatory power, (3) all genetically-informed ecological niche models (gENMs) outperformed the standard species-level ENM (3-14% increase in AUC; 1-23% increase in pROC). Furthermore, (4) gENMs predicted large differences among ecotypes in both the direction and magnitude of responses to climate change, and (5) revealed evidence of niche divergence, particularly for the Eastern Rocky Mountain ecotype. (6) Models also predicted progressively increasing fragmentation and decreasing overlap between ecotypes. Contact zones are often hotspots of diversity that are critical for supporting species' capacity to respond to present and future climate change, thus predicted reductions in connectivity among ecotypes is of conservation concern. We further examined the generality of our findings by comparing our model developed for a higher elevation Rocky Mountain species with a related desert riparian cottonwood, P. fremontii. Together our results suggest that incorporating intraspecific genetic information can improve model performance by addressing this important source of variance. gENMs bring an evolutionary perspective to niche modeling and provide a truly "adaptive management" approach to support conservation genetic management of species facing global change.

Data from: Modelling the current and future biodiversity distribution in the Chilean Mediterranean Hotspot. The role of protected areas network in a warmer future

Aim: Mediterranean Chile is part of the five recognized Mediterranean-type climates in the world and harbors a very rich floral diversity. Climate change has been reported as a significant threat to its biodiversity. We used the flora of Mediterranean Chile to analyze how biodiversity patterns, as measured by Phylogenetic Diversity, genus and species richness will respond to climate change scenarios and identify the areas that will harbor the greatest evolutionary potential and biodiversity richness. We also evaluated how these spatial patterns are depicted within the current network of protected areas. Location: Chilean Mediterranean climate-type Region, South America. Methods: Biodiversity metrics were evaluated for current and future climatic scenarios. Species distribution models were done using Maxent for 1.727 species and 571 genera. Relationships between species/genera gain, loss and turnover were evaluated. For Mediterranean endemic species, loss and gain was also related to life form. Finally, variation in species gain, loss and turnover was evaluated in future climate change scenarios within and outside Mediterranean Chile state protected areas. Results: We found a general decrease in species richness in the entire Region toward future climate change scenarios. Phylogenetic Diversity is predicted to be higher than expected by richness in the north and south of the area, and lower than expected by richness in the Andes mountain. The highest average species and genus loss is predicted to occur outside the protected areas, meanwhile species and genus gain is higher within them. Main conclusions: Future biodiversity patterns are reported here for the first time in the Chilean Mediterranean Region. Our findings enhance the importance of the current protected areas to harbor this future variation, despite their reduced number and size along the region.

opencc-by-4.0Aug 2020View details →

Data for: Model-Based Reconstruction for Simultaneous Multi-Slice T1 Mapping using Single-Shot Inversion-Recovery Radial FLASH

Magnetic Resonance Imaging measurement data used in our paper about "Model-based reconstruction for simultaneous multi-slice T1 mapping using single-shot inversion-recovery radial FLASH" (DOI: <a href="https://onlinelibrary.wiley.com/doi/full/10.1002/mrm.28497">10.1002/mrm.28497</a>). The data is provided in a file format used by the BART toolbox (DOI: <a href="http://doi.org/10.5281/zenodo.592960">10.5281/zenodo.592960</a>).

opencc-zeroSep 2020View details →

Data from: Lessons from movement ecology for the return to work: modeling contacts and the spread of COVID-19

Human behavior (movement, social contacts) plays a central role in the spread of pathogens like SARS-CoV-2. The rapid spread of SARS-CoV-2 was driven by global human movement, and initial lockdown measures aimed to localize movement and contact in order to slow spread. Thus, movement and contact patterns need to be explicitly considered when making reopening decisions, especially regarding return to work. Here, as a case study, we consider the initial stages of resuming research at a large research university, using approaches from movement ecology and contact network epidemiology. First, we develop a dynamical pathogen model describing movement between home and work; we show that limiting social contact, via reduced people or reduced time in the workplace are fairly equivalent strategies to slow pathogen spread. Second, we develop a model based on spatial contact patterns within a specific office and lab building on campus; we show that restricting on-campus activities to labs (rather than labs and offices) could dramatically alter (modularize) contact network structure and thus, potentially reduce pathogen spread by providing a workplace mechanism to reduce contact. Here we argue that explicitly accounting for human movement and contact behavior in the workplace can provide additional strategies to slow pathogen spread that can be used in conjunction with ongoing public health efforts.

opencc-zeroSep 2020View details →

Data from: Weighing homoplasy against alternative scenarios with the help of macroevolutionary modeling: a case study on limb bones of fossorial sciuromorph rodents

Homoplasy is a strong indicator of a phenotypic trait's adaptive significance when it can be linked to a similar function. We assessed homoplasy in functionally relevant scapular and femoral traits of Marmotini and Xerini, two sciuromorph rodent clades that independently acquired a fossorial lifestyle from an arboreal ancestor. We studied 125 species in the scapular dataset and 123 species in the femoral dataset. Pairwise evolutionary model comparison was used to evaluate whether homoplasy of trait optima is more likely than other plausible scenarios. The most likely trend of trait evolution among all traits was assessed via likelihood scoring of all considered models. The homoplasy hypothesis could never be confirmed as the single most likely model. Regarding likelihood scoring, scapular traits most frequently did not differ among Marmotini, Xerini, and arboreal species. For the majority of femoral traits, results indicate that Marmotini, but not Xerini, evolved away from the ancestral arboreal condition. We conclude on the basis of the scapular results that the forelimbs of fossorial and arboreal sciuromorphs share mostly similar functional demands, whereas the results on the femur indicate that the hind limb morphology is less constraint, perhaps depending on the specific fossorial habitat.

opencc-by-4.0Oct 2020View details →

Data Supplement for "A unified numerical model for wetting of soft substrates"

This dataset contains the data for Fig. 3(a) of our publication Aland, S. & Mokbel, D. A unified numerical model for wetting of soft substrates (submitted to International Journal for Numerical Methods in Engineering 2020). The data are stored in the files "PresentSimulation.csv", \(\)"ReferenceSolution.csv" and "Experiments.csv". We provide a MLX-File (Live Code File Format), which can be called in a MATLAB editor by entering "Fig3a_live". Alternatively, we also provide a M-File that can be used in the same manner, entering "Fig3a" in a MATLAB editor. We used MatlabR2019b.

opencc-by-4.0Oct 2020View details →

Climate model data from "Changes in local and global climate feedbacks in the absence of interactive clouds: Southern Ocean-climate interactions in two intermediate-complexity models"

This Dataset contains the model output described in the study "Changes in local and global climate feedbacks in the absence of interactive clouds: Southern Ocean-climate interactions in two intermediate-complexity models" by Pfister and Stocker 2020, published in Journal of Climate. The two zip files contain the model output of the two models Bern3D-LPX and LOVECLIM, in folder structures explained below. Bern3D-LPX: The 3 folders contain model simulations tuned to different ECS values (2, 3 and 6 Kelvin). Each folder contains three subfolders corresponding to three simulations: Control, 2xCO2 and 4xCO2. For each simulation, two netcdf model output files are given: a timeseries file for quick overview of various spatially averaged variables (e.g., global mean temperature), and a full output file for local analyses as done in the study. For the main simulations with an ECS of 3 Kelvin, annual mean output is provided for the first 500 years of each simulation. Thereafter, the full output is available only for selected years, which can be read out from the netcdf time dimension or, e.g., the netcdf variable "baseyear". Simulations with an ECS of 2 and 6 Kelvin are only used for Figure 8 and its discussion, therefore their full output file was written with less yearly outputs than the main simulation with ECS=3 Kelvin to reduce data load. LOVECLIM: The 2 folders contain the 2xCO2 and 4xCO2 simulations. No separate Control simulations were made, but the first 1000 years of each simulation are unperturbed and used as a control reference (details in Pfister and Stocker 2020, J.Clim.). The two netcdf files for each simulation correspond to atmospheric variables (atmmmyl_cat.nc) and ocean variables (CLIO3m_cat_CO2_2_regridded.nc). Note that the spatial resolution of the atmosphere and ocean component of LOVECLIM are different. Monthly output is provided for the given variables of the full 2000-year-simulations.   For a detailed description how these model outputs were analyzed, please refer to Pfister and Stocker 2020, J. Clim.

opencc-zeroOct 2020View details →

Multiscale heart image data for: Multiscale cardiac imaging spanning the whole heart and its internal cellular architecture in a small animal model

Cardiac pumping depends on the morphological structure of the heart, but also on its sub-cellular (ultrastructural) architecture, which enables cardiac contraction. In cases of congenital heart defects, localized ultrastructural disruptions that increase the risk of heart failure are only starting to be discovered. This is in part due to a lack of technologies that can image the three dimensional (3D) heart structure, assessing malformations; and its ultrastructure, assessing disruptions. We present here a multiscale, correlative imaging procedure that achieves high-resolution images of the whole heart, using 3D micro-computed tomography (micro-CT); and its ultrastructure, using 3D scanning electron microscopy (SEM). We achieved uniform fixation and staining of the whole heart, without losing ultrastructural preservation on the same sample, enabling correlative multiscale imaging. Our approach enables multiscale studies in models of congenital heart disease and beyond.

opencc-zeroOct 2020View details →

Data: Using environmental DNA and occupancy modeling to estimate rangewide metapopulation dynamics

We demonstrate the power of combining two emergent tools for resolving rangewide metapopulation dynamics. First, we employed environmental DNA (eDNA) surveys to efficiently generate multi-season rangewide site occupancy histories. Second, we developed a novel dynamic, spatial multiscale occupancy model to estimate metapopulation dynamics. The model incorporates spatial relationships, explicitly accounts for non-detection bias and allows direct evaluation of the drivers of extinction and colonization. We applied these tools to examine metapopulation dynamics of endangered tidewater goby, a species endemic to California estuarine habitats. We analyzed rangewide eDNA data from 190 geographically isolated sites (813 total water samples) surveyed from two years (2016 and 2017). Rangewide estimates of the proportion of sites that were occupied varied little between 2016 (0.52) and 2017 (0.51). However, there was evidence of extinction and colonization dynamics. The probability of extinction of an occupied site (0.106) and probability of colonization of an unoccupied site (0.085) were nearly equal. Stability in site occupancy proportions combined with nearly equal rates of extinction and colonization suggests a dynamic equilibrium between the two years surveyed. Assessment of covariate effects revealed that colonization probability increased as the number of occupied neighboring sites increased and as distance between occupied sites decreased. We show that eDNA surveys can rapidly provide a snapshot of a species distribution over a broad geographic range, and when these surveys are paired with occupancy modeling, can uncover metapopulation dynamics and their drivers.

opencc-by-4.0Oct 2020View details →

Data for "Bayesian inference of mantle viscosity from whole-mantle density models"

Supplementary Data Rudolph, M.L., Moulik, P., and Lekic, V. (2020). Bayesian inference of mantle viscosity from whole-mantle density models. Geochemistry, Geophysics, Geosystems This data archive contains files needed to reproduce the figures from our 2020 G-Cubed paper, including the full ensemble solutions for mantle viscosity structure.