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opencc-zeroDec 2013View details →

Data from: Temporally dynamic habitat suitability predicts genetic relatedness among caribou

Landscape heterogeneity plays a central role in shaping ecological and evolutionary processes. While species utilization of the landscape is usually viewed as constant within a year, the spatial distribution of individuals is likely to vary in time in relation to particular seasonal needs. Understanding temporal variation in landscape use and genetic connectivity has direct conservation implications. Here, we modelled the daily use of the landscape by caribou in Quebec and Labrador, Canada and tested its ability to explain the genetic relatedness among individuals. We assessed habitat selection using locations of collared individuals in migratory herds and static occurrences from sedentary groups. Connectivity models based on habitat use outperformed a baseline isolation-by-distance model in explaining genetic relatedness, suggesting that variations in landscape features such as snow, vegetation productivity and land use modulate connectivity among populations. Connectivity surfaces derived from habitat use were the best predictors of genetic relatedness. The relationship between connectivity surface and genetic relatedness varied in time and peaked during the rutting period. Landscape permeability in the period of mate searching is especially important to allow gene flow among populations. Our study highlights the importance of considering temporal variations in habitat selection for optimizing connectivity across heterogeneous landscape and counter habitat fragmentation.

opencc-zeroJul 2019View details →

Data from: Coffee berry borer (Hypothenemus hampei) (Coleoptera: Curculionidae) development across an elevational gradient on Hawai'i Island: applying laboratory degree-day predictions to natural field populations

Coffee berry borer (CBB, Hypothenemus hampei) (Coleoptera: Curculionidae: Scolytinae) is the most destructive pest of coffee worldwide. Information on CBB development times can be used to predict the initiation of new infestation cycles early in the coffee-growing season and thus inform the timing of insecticide applications. While laboratory estimates of CBB development under constant conditions exist, they have not been applied under the heterogeneous environmental conditions that characterize many coffee-growing regions. We measured CBB development times and abundance in commercial coffee farms across an elevational gradient on Hawai'i Island and applied thermal accumulation models from previous laboratory studies to test their fit to field data. Artificial lures were used to infest coffee berries at five farms ranging in elevation from 279-792 m, and weather variables were monitored at macro (farm-level) and micro (branch-level) scales. CBB development was followed in the field from the time of initial berry infestation by the founding female through the development of F1 mature adults. Mean development time from egg to adult across all sites was 38.5 ± 3.46 days, while the mean time required for the completion of a full life cycle (from time of infestation to presence of mature F1 females) was 50.9 ± 3.35 days. Development time increased with increasing elevation and decreasing temperature. Using macro-scale temperature data and two different estimates for the lower temperature threshold (14.9°C and 13.9°C), we estimated a mean requirement of 332 ± 14 degree-days and 386 ± 16 degree-days, respectively, from the time of berry infestation to the initiation of a new reproductive cycle in mature coffee berries. Similar estimates were obtained using micro-scale temperature data, indicating that macro-scale temperature monitoring is sufficient for life-cycle prediction. We also present a model relating elevation to number of CBB generations per month. Our findings suggest that CBB development times from laboratory studies are generally applicable to field conditions on Hawai'i Island and can be used as a decision support tool to improve IPM strategies for this worldwide pest of coffee.

opencc-zeroDec 2013View details →

Data from: Immune response genes and pathogen presence predict migration survival in wild salmon smolts

We present the first data to link physiological responses and pathogen presence with subsequent fate during migration of wild salmonid smolts. We tagged and non-lethally sampled gill tissue from sockeye salmon (Oncorhynchus nerka) smolts as they left their nursery lake (Chilko Lake, BC, Canada) to compare gene expression profiles and freshwater pathogen loads with migration success over the first ~1150 km of their migration to the North Pacific Ocean using acoustic telemetry. Fifteen percent of smolts were never detected again after release and these fish had gene expression profiles consistent with an immune response to one or more viral pathogens compared with fish that survived their freshwater migration. Among the significantly up-regulated genes of the fish that were never detected post-release were MX (Interferon-induced GTP-binding Protein Mx) and STAT1 (Signal transducer and activator of transcription 1-alpha/beta), which are characteristic of a type I interferon response to viral pathogens. The most commonly detected pathogen in the smolts leaving the nursery lake was infectious hematopoietic necrosis virus (IHNV). Collectively, these data show that some of the fish assumed to have died after leaving the nursery lake appeared to be responding to one or more viral pathogens and had elevated stress levels that could have contributed to some of the mortality shortly after release. We present the first evidence that changes in gene expression may be predictive of some of the fresh water migration mortality in wild salmonid smolts.

opencc-zeroDec 2016View details →

Data from: Non-breeding range size predicts the magnitude of population trends in trans-Saharan migratory passerine birds

Understanding why populations of some migratory species show a directional change over time, i.e. increase or decrease, while others do not, remains a challenge for ecological research. One possible explanation is that species with smaller non-breeding ranges may have more pronounced directional population trends, and their populations are thus more sensitive to the variation in environmental conditions in their non-breeding quarters. According to the serial residency hypothesis, this sensitivity should lead to higher magnitudes (i.e. absolute values) of population trends for species with smaller non-breeding ranges, with the direction of trend being either positive or negative depending on the nature of the environmental change. We tested this hypothesis using population trends over 2001–2012 for 36 sub-Saharan migratory passerine birds breeding in Europe. Namely, we related the magnitude of the species' population trends to the size of their sub-Saharan non-breeding grounds, whilst controlling for factors including number of migration routes, non-breeding habitat niche and wetness, breeding habitat type and life-history strategy. The magnitude of species' population trends grew with decreasing absolute size of sub-Saharan non-breeding ranges, and this result remained significant when non-breeding range size was expressed relative to the size of the breeding range. After repeating the analysis with the trend direction, the relationship with the non-breeding range size disappeared, indicating that both population decreases and increases are frequent amongst species with small non-breeding range sizes. Therefore, species with small non-breeding ranges are at a higher risk of population decline due to adverse factors such as habitat loss or climatic extremes, but their populations are also more likely to increase when suitable conditions appear. As non-breeding ranges may originate from stochasticity of non-breeding site selection in naive birds ('serial-residency' hypothesis), it is crucial to maintain a network of stable and resilient habitats over large areas of birds' non-breeding quarters.

opencc-zeroOct 2019View details →

Data from: Forest-type specialization strongly predicts avian responses to tropical agriculture

Species' traits influence how populations respond to land-use change. However, even in well-characterized groups such as birds, widely studied traits explain only a modest proportion of the variance in response across species. Here, we show that associations with particular forest types strongly predict the sensitivity of forest-dwelling Amazonian birds to agriculture. Incorporating these fine-scale habitat associations into models of population response dramatically improves predictive performance and markedly outperforms the functional traits that commonly appear in similar analyses. Moreover, by identifying habitat features that support assemblages of unusually sensitive habitat-specialist species, our model furnishes straightforward conservation recommendations. In Amazonia, species that specialize on forests along a soil–nutrient gradient (i.e. both rich-soil specialists and poor-soil specialists) are exceptionally sensitive to agriculture, whereas species that specialize on floodplain forests are unusually insensitive. Thus, habitat specialization per se does not predict disturbance sensitivity, but particular habitat associations do. A focus on conserving specific habitats that harbour highly sensitive avifaunas (e.g. poor-soil forest) would protect a critically threatened component of regional biodiversity. We present a conceptual model to explain the divergent responses of habitat specialists in the different habitats, and we suggest that similar patterns and conservation opportunities probably exist for other taxa and regions.

opencc-zeroJun 2021View details →

Prediction model of in-hospital mortality in intensive care unit patients with heart failure: machine learning-based, retrospective analysis of the MIMIC-III database

Objective: The predictors of in-hospital mortality for intensive care units (ICU)-admitted HF patients remain poorly characterized.We aimed to develop and validate a prediction model for all-cause in-hospital mortality among ICU-admitted HF patients. Design: A retrospective cohort study. Setting and Participants: Data were extracted from the MIMIC-III database. Data on 1,177 heart failure patients were analysed. Methods: Patients meeting the inclusion criteria were identified from the MIMIC-III database and randomly divided into derivation and validation groups. Independent risk factors for in-hospital mortality were screened using XGBoost and LASSO regression models in the derivation sample. Multivariable logistic regression analysis was used to build prediction models. Discrimination, calibration, and clinical usefulness of the predicting model were assessed using the C-index, calibration plot, and decision curve analysis. After pairwise comparison, the best performing model was chosen to build a nomogram according to the regression coefficients. Results: Among the 1,177 admissions, in-hospital mortality was 13.52%. In both groups, the XGBoost, LASSO regression, and GWTG-HF risk score models showed acceptable discrimination. The XGBoost and LASSO regression models also showed good calibration. In pairwise comparison, the prediction effectiveness was higher with the XGBoost and LASSO regression models than with the GWTG-HF risk score model (P<0.05). The XGBoost model was chosen as our final model for its more concise and wider net benefit threshold probability range and was presented as the nomogram. Conclusions: Our nomogram enabled good prediction of in-hospital mortality in ICU-admitted HF patients, which may help clinical decision-making for such patients.

opencc-zeroFeb 2022View details →

Data from: Sudden unexpected death in epilepsy: a personalised prediction tool

<div> Objective: To develop and validate a tool for individualised prediction of Sudden Unexpected Death in Epilepsy (SUDEP) risk, we re-analysed data from one cohort and three case-control studies undertaken 1980-2005.</div> Methods: We entered 1273 epilepsy cases (287 SUDEP, 986 controls) and 22 clinical predictor variables into a Bayesian logistic regression model. Results: Cross-validated individualized model predictions were superior to baseline models developed from only average population risk or from generalised tonic-clonic seizure frequency (pairwise difference in leave-one-subject-out expected log posterior density = 35.9, SEM +/-12.5, and 22.9, SEM +/-11.0 respectively). The mean cross-validated (95% Credibility Interval) Area Under the Receiver Operating Curve was 0.71 (0.68 to 0.74) for our model versus 0.38 (0.33 to 0.42) and 0.63 (0.59 to 0.67) for the baseline average and generalised tonic-clonic seizure frequency models respectively. Model performance was weaker when applied to non-represented populations. Prognostic factors included generalized tonic-clonic and focal-onset seizure frequency, alcohol excess, younger age of epilepsy onset and family history of epilepsy. Anti-seizure medication adherence was associated with lower risk. Conclusions: Even when generalised to unseen data, model predictions are more accurate than population-based estimates of SUDEP. Our tool can enable risk-based stratification for biomarker discovery and interventional trials. With further validation in unrepresented populations it may be suitable for routine individualized clinical decision-making. Clinicians should consider assessment of multiple risk factors, and not only focus on the frequency of convulsions.

Male age alone predicts paternity success under sperm competition when effects of age and past mating effort are experimentally separated

Older males often perform poorly under post-copulatory sexual selection. It is unclear, however, whether reproductive senescence is due to male age itself or the accumulated costs of the higher lifetime mating effort that is usually associated with male age. To date, very few studies have accounted for mating history and sperm storage when testing the effect of male age on sperm traits, and none test how age and past mating history influence paternity success under sperm competition. Here, we experimentally manipulate male mating history to tease apart its effects from that of age on ejaculate traits and paternity in the mosquitofish, Gambusia holbrooki. We found that old, naive males had more sperm than old, experienced males, while the reverse was true for young males. In contrast, neither male age nor mating history affected sperm velocity. Finally, using artificial insemination to experimentally control the number of sperm per male, we found that old males sired significantly more offspring than young males independently of their mating history. Our results highlight that the general pattern of male reproductive senescence described in many taxa may often be affected by two naturally confounding factors, male mating history and sperm age, rather than male age itself.

opencc-by-nc-4.0Jul 2021View details →

fMRI free-viewing data, resting-state and structural in macaques associated with publication 'Social prediction modulates activity of macaque superior temporal cortex'

Using a free-viewing and functional magnetic resonance imaging, we show that activity in a region of the macaque middle superior temporal (midSTS) cortex was specifically modulated by the predictability of social interactions. This region could be distinguished from other temporal regions involved in face processing. Using resting-state fMRI in anesthetized macaques, we showed that the connectivity between the face-responsive areas and the social prediction area was more integrated in macaques than in humans. We reproduce the social prediction results in a replication study and provide a control to rule out oculomotor implication through the FEF in the social prediction activity of the midSTS, using Transcranial Ultrasound Stimulation. Using standard geometric shape movement stimuli, we also show that macaques do not attribute mental state to shapes.

opencc-by-4.0Jul 2021View details →

Material for the paper "MIDI2vec: Learning MIDI Embeddings for Reliable Prediction of Symbolic Music Metadata"

Edgelists and trained embeddings used in the paper

Unraveling the predictive role of temperature in the gut microbiota of the sea urchin Echinometra sp. EZ across spatial and temporal gradients

Shifts in microbial communities represent a rapid response mechanism for host organisms to respond to changes in environmental conditions. Therefore, they are likely to be important in assisting the acclimatization of hosts to seasonal temperature changes as well as to variation in temperatures across a species' range. The Persian/Arabian Gulf is the world's warmest sea, with large seasonal fluctuations in temperature (20℃ - 37℃) and is connected to the Gulf of Oman which experiences more typical oceanic conditions (<32℃ in the summer). This system is an informative model for understanding how symbiotic microbial assemblages respond to thermal variation across temporal and spatial scales. Here, we elucidate the role of temperature on the microbial gut community of the sea urchin Echinometra sp. EZ and identify microbial taxa that are tightly correlated with the thermal environment. We generated two independent datasets with a high degree of geographic and temporal resolution. The results show that microbial communities vary across thermally variable habitats, display temporal shifts that correlate with temperature, and can become more disperse as temperatures rise. The relative abundances of several ASVs significantly correlate with temperature in both independent datasets despite the >300 km distance between the furthest sites and the extreme seasonal variations. Notably, over 50% of the temperature predictive ASVs identified from the two datasets belonged to the family Vibrionaceae. Together, our results identify temperature as a robust predictor of community-level variation and highlight specific microbial taxa putatively involved in the response to thermal environment.

Personality predicts innovation and social learning in children; Implications for cultural evolution

Innovation and social learning are the pillars of cultural evolution, allowing cultural behaviours to cumulatively advance over generations. Yet, little is known about individual differences in the use of social and asocial information. We examined whether personality influenced children's propensity to observe others or independently generate solutions to novel problems. Conscientiousness was associated with electing for no demonstrations, while agreeableness was associated with opting for demonstrations. For children receiving demonstrations, openness to experience consistently predicted deviation from observed methods. Children who opted for no demonstrations were also more likely than those opting for demonstrations to exhibit tool manufacture on an innovation challenge and displayed higher creativity. These results highlight how new cultural traditions emerge, establish and advance by identifying which individuals generate new cultural variants in populations and which are influential in the diffusion of these variants, and help reduce the apparent tension within the 'ratchet' of cumulative culture.

opencc-by-4.0Jul 2021View details →

A Deep Learning Approach to the Forward Prediction and Inverse Design of Plasmonic Metasurface Structural Color - Raw Data

Reflection spectra of PDMS - Al nanorod metamaterials were collected using LUMERICAL FDTD simulations. PDMS material properties were defined using a refractive index of 1.41 and Al material properties were defined using frequency selective permittivities from the handbook of Palik. A total of 4620 structures were simulated, sweeping the following dimension parameters: <ul> <li>Aluminium thickness (t)</li> <li>Pillar height (h)</li> <li>Pillar diameter (d)</li> </ul> Reflectance spectra were converted into CIE 1931 chromaticity values (x,y). This dataset is comprehensive and allows for the development of deep learning models for the forward and inverse design of the given metamaterial structure as detailed in the associated manuscript. The associated manuscript and supporting documentation provide extensive details of data collection and processing methods.

Competitiveness prediction for nodule colonization in Sinorhizobium meliloti through combined in vitro tagged strain characterization and genome-wide association analysis

Associations between leguminous plants and symbiotic nitrogen-fixing rhizobia are a classic example of mutualism between a eukaryotic host and a specific group of prokaryotic microbes. Although this symbiosis is in part species-specific, different rhizobial strains may colonise the same nodule. Some rhizobial strains are commonly known as better competitors than others, but detailed analyses that aim to predict rhizobial competitive abilities based on genomes are still scarce. Here, we performed a bacterial genome-wide association (GWAS) analysis to define the genomic determinants related to the competitive capabilities in the model rhizobial species Sinorhizobium meliloti. For this, 13 tester strains were GFP-tagged and assayed vs. 3 RFP-tagged reference competitor strains (Rm1021, AK83, and BL225C) in a Medicago sativa nodule occupancy test. Competition data and strain genomic sequences were employed to build a model for GWAS based on k-mers. Among the k-mers with the highest scores, 51 k-mers mapped on the genomes of four strains showing the highest competition phenotypes (> 60% single strain nodule occupancy; GR4, KH35c, KH46 and SM11) vs. BL225C. These k-mers were mainly located on the symbiosis-related megaplasmid pSymA, specifically on genes coding for transporters, proteins involved in the biosynthesis of cofactors and proteins related to metabolism (e.g., fatty acids). The same analysis was performed considering the sum of single and mixed nodules obtained in the competition assays vs. BL225C, retrieving k-mers mapped on the genes previously found and on vir genes. Therefore, the competition abilities seem to be linked to multiple genetic determinants and comprise several cellular components.

Data from: Assessing bite force estimates in extinct mammals and archosaurs using phylogenetic predictions

Bite force is an ecologically important biomechanical performance measure is informative in inferring the ecology of extinct taxa. However, biomechanical modelling to estimate bite force is associated with some level of uncertainty. Here, I assess the accuracy of bite force estimates in extinct taxa using a Bayesian phylogenetic prediction model. I first fitted a phylogenetic regression model on a training set comprising extant data. The model predicts bite force from body mass and skull width while accounting for differences owning to biting position. The posterior predictive model has a 93% prediction accuracy as evaluated through leave-one-out cross-validation. I then predicted bite force in 37 species of extinct mammals and archosaurs from the posterior distribution of predictive models. Biomechanically estimated bite forces fall within the posterior predictive distributions for all except four species of extinct taxa and are thus as accurate as that predicted from body size and skull width, given the variation inherent in extant taxa and the amount of time available for variance to accrue. Biomechanical modelling remains a valuable means to estimate bite force in extinct taxa and should be reliably informative of functional performances and serve to provide insights into past ecologies.

opencc-zeroMay 2022View details →

Climatic niche shifts in 815 introduced plant species affect their predicted distributions: Data and scripts

Aim: Introduced species often occupy different climates in their introduced than their native range, but to what degree do such 'climatic niche shifts' interfere with our ability to predict invasions? Answering this question is crucial if we are to understand the threat invasive species pose to human and natural systems, especially given the ever increasing use of species distribution models as tools for invasive species risk assessment and management. Here we investigated how strongly climatic niche shifts interfered with the transferability of native- and introduced-range species distribution models. Location: Our dataset consisted of ~14 million occurrences distributed worldwide. Time Period: Occurrence data were collected from online repositories dating from ca. 1600 with the vast majority being from the 20th century. Climatic data represent means between 1970–2000. Major Taxa Studied: Our database represented 815 terrestrial plant species. Methods: We used ordination to identify climatic niche shifts as species moved between continents. Next, we trained separate MAXENT models using native- or introduced-range occurrences, and projected those models into each species' introduced range. We compared the ordination and MAXENT models to determine whether niche shifts were associated with errors in MAXENT predictions. Results: Models trained on native-range occurrences poorly predicted introduced-range occurrences, and transferability was lowest in species with large climatic niche shifts. Directional shifts in species' predicted geographic distributions mirrored their niche dynamics. This is concerning because native-range data are often used to predict introduced-range distributions. Main Conclusions: Our results highlight the importance of considering niche shifts when modeling the potential geographic distributions of introduced species, and cast doubt on the assumption that the climatic niche of a species can be transferred between native and invasive ranges.

opencc-by-nc-4.0Nov 2020View details →

Data of "Evaluation of feeding behaviour traits to predict efficiency traits in pigs using partial least square regression"

This study explores the potential of using automatically recorded feeding behaviour as a proxy trait for protein and energy efficiency as well as fat gain in Swiss Large White pigs. behaviour.txt: A tab-delimited txt file of data used to investigate the relationship between feeding behaviour traits and protein efficiency in Swiss Large White pigs. Feeding behaviour traits taken were ‘daily feed intake’, ‘feeding rate’, ‘number of daily visits’, ‘duration at visits’, ‘feeding occupation’, and ‘feed intake per visit’. data_description_for_feeding_behaviour.xlsx: meta data for behaviour.txt with descriptions of variables

Is phylogeographic congruence predicted by historical habitat stability, or ecological co-associations?

Comparative phylogeographic studies can uniquely distinguish idiosyncratic versus community-wide responses to past environmental change. However, to date, impacts of species interactions have been largely overlooked. Here we used non-genetic data to characterize two competing scenarios about expected levels of congruence among five saproxylic invertebrate species (i.e., a wood-feeding cockroach, termite and beetle; a predatory centipede, and a detritivorous millipede) from the southern Appalachians mountains—a topographically complex unglaciated landscape. Under one scenario, abiotic factors primarily drove species' responses, with predicted congruence based on spatial overlap of climatically stable habitat areas estimated for each species via ecological niche modeling. The other scenario considered biotic factors to be most influential, with proxies for actual or potential direct interactions used to predict congruence. Analyses of mitochondrial and nuclear DNA sequence datasets for each species focused on four axes of comparison: the number and distribution of spatial-genetic clusters, phylogeographic structure, changes in long-term effective population size, and historical gene flow dynamics. Overall, we found stronger support for ecological co-associations scenario, suggesting an important influence of biotic factors in constraining or facilitating species' responses to Pleistocene climatic cycles. However, there was an imperfect fit between this scenario's predictions and outcomes of empirical data analyses. Thus, our conclusions are compelling, but tentative. This work advances comparative phylogeography by expanding the scope of inferences beyond abiotic drivers, and provides insights into the evolutionary history of a functionally important ecological community, within a globally recognized center of endemism.

Do the predicted suitability scores from species distribution models correlate with species performance on-ground?

Species distribution models are a very popular statistical tool for inferring potential distribution range of species across space and time and are thought to be a good predictor for habitat suitability. Some studies have suggested that if these models are reliable, predicted habitat suitability (PHS) should relate to species traits visualization, growth potential, body size, abundance. We validated this hypothesis by estimating association between the PHS and species abundance for 17 avian species endemic to the Western Ghats - Sri Lanka biodiversity hotspot. Additionally, we compared the PHS of sites where species were detected in both seasons (wet and dry) against sites where they were detected in the dry season alone. As a proxy for abundance, we estimated single-season occupancy estimates (ψ) using detection/non-detection data from multiple visits to the survey sites. We report significant and positive PHS-ψ correlation, though the strength of this association varied across species and models. Half of the species showed higher suitability scores for the sites where they were detected year round. The results presented here suggest that the predictive models can be used as a proxy for habitat quality, in addition to inferring the potential distribution.

A trait‐based approach predicting community assembly and dominance of microbial invasive species

Understanding the mechanisms underlying community assembly helps to define success and susceptibility to biological invasions. A methodological approach to this aim is to use trait-based approaches. Under the hypothesis that the morphology-based functional groups (MBFG) clusters species with similar niche we analyse how trait-related differences in fitness influence the outcome of invasion. The invasive dinoflagellate Ceratium furcoides (CF) can be used as the model species considering its morphological (e.g. volume) and physiological traits (e.g. growth rates) comparing with species from the same (MBFG V) and different (MBFG VII: colonial cyanobacteria) MBFG. Here we present the information needed to apply this approach with similar or different questions including information from two aquatic environments from South America: the first one located in Argentina (Miní flood-plain Lake) and the second one in Uruguay (Salto Grande Reservoir). Phytoplankton morphological traits measured from field samples, along with richness, abundance, biovolume and environmental variables are presented. Phytoplankton individuals and species are classified in terms of MBFG and focus on the invasive species Ceratium furcoides. The literature derived information includes growth rates with temperature for phytoplankton species classified in MBFG V including Ceratium furcoides.