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38 results for “opportunistic data”
Data and R script for 'Opportunistic food consumption in relation to childhood and adult food insecurity: An exploratory correlational study'
<p>One raw data file and one R script that reproduces all analyses and figures reported in the paper '<strong>Opportunistic food consumption in relation to childhood and adult food insecurity: An exploratory correlational study</strong>' by Nettle et al. </p>
Data package for opportunistic constant target matchup study
<p>Opportunistic constant target matching is a new method for satellite<br> intercalibration.</p> <p>It is complementary to the traditional simultaneous nadir overpass<br> (SNO) method because it can provide warm matchups in cases where the<br> SNO method provides only cold matchups.</p> <p>A geostationary infrared sensor (SEVIRI) is used to select constant<br> target matches for two different microwave sensors (NOAA 18 and Metop<br> A). This is the data package for a publication where we discuss the<br> main assumptions and limitations of the new method and explore its<br> statistical properties with a simple Monte Carlo simulation and with<br> real observations from NOAA 18 and Metop A.</p>
Data from: Patterns of annual and seasonal immune investment in a temporal reproductive opportunist
Historically, investigations of how organismal investments in immunity fluctuate in response to environmental and physiological changes have focused on seasonally breeding organisms that confine reproduction to seasons with mild environmental conditions and abundant resources. The red crossbill, <i>Loxia curvirostra</i>, is a songbird that can breed opportunistically if conifer seeds are abundant, on both short, cold, and long, warm days, providing an ideal system to investigate interactions between immunity, reproduction, and environmental fluctuations. In this study, we measured inter- and intra-annual variation in complement, natural antibodies, PIT54, and leukocytes in crossbills across four summers (2010-2013) and multiple seasons within one year (summer 2011-spring 2012). Overall, we observed substantial changes in crossbill immune investment among summers, with interannual variation driven largely by food resources, while seasonal variation was less pronounced and lacked a dominant predictor of immune investment. However, we found weak evidence that physiological processes (e.g., reproductive condition, moult) or abiotic factors (e.g., temperature, precipitation) affect immune investment. Collectively, this study suggests that a reproductively flexible organism may simultaneously invest in both reproduction and survival-related processes, potentially by exploiting rich patches with abundant resources. More broadly, these results emphasize the need for more longitudinal studies of trade-offs associated with immune investment.
Data from: Opportunistic data reveal widespread species turnover in Enallagma damselflies at biogeographical scales
An information tradeoff exists between systematic presence/absence surveys and purely opportunistic (presence-only) records for investigating the geography of community structure. Opportunistic species occurrence data may be of relatively limited quality, but typically involves numerous observations and species. Given the quality-quantity tradeoff, what can opportunistic data reveal about spatial patterns in community structure? Here we explore opportunistic data in describing geographic patterns of species composition, using over 4,600 occurrence records of Enallagma damselflies in the United States. We tested phylogenetic scale (genus level, Enallagma major clades, Enallagma subclades) and spatial extent (U.S. vs. watershed regions), hypothesizing that nonrandom structure is more likely at larger spatial extents. We also used three sets of systematic presence/absence surveys as a benchmark for validating opportunistic presence-only records. Null model analysis of matrix coherence and species replacements showed many cases of nonrandom structure and widespread species turnover. This outcome was repeated across spatial and environmental gradients and community composition scenarios. Turnover dominated across the U.S. and two watersheds spanning biogeographic boundaries, but random assemblages were prevalent in a third watershed with limited longitudinal extent. Turnover also pervaded each level of phylogeny. Opportunistic presence-only datasets showed identical patterns as systematic presence/absence datasets. These results indicate that extensive opportunistic data can be used to detect species turnover, especially at geographic scales where range margins are crossed.
Data from: Assessing the usefulness of Citizen Science Data for habitat suitability modelling: opportunistic reporting versus sampling based on a systematic protocol
<p><strong>Aim:</strong> To evaluate the potential of models based on opportunistic reporting (OR) compared to models based on data from a systematic protocol (SP) for modelling species distributions. We compared model performance for eight forest bird species with contrasting spatial distributions, habitat requirements, and rarity. Differences in the reporting of species were also assessed. Finally, we tested potential improvement of models when inferring high quality absences from OR based on questionnaires sent to observers.</p> <p><strong>Location:</strong> Both datasets cover the same large area (Sweden) and time period (2000 -2013).</p> <p><strong>Methods:</strong> Species distributions were modelled using logistic regression. Predictive performance of OR models to predict SP data were assessed based on AUC. We quantified the congruence in spatial predictions using Spearman's rank correlation coefficient. We related these results to species characteristics and reporting behaviour of observers. We also assessed the gain in predictive performance of OR models by adding inferred absences. Finally, we investigated the potential impact of sampling bias in OR.</p> <p><strong>Results:</strong> For all species, and despite the sampling biases, results from OR overall agreed well with those of SP, for the nationwide spatial congruence of habitat suitability maps and the selection and directions of species-environment relationships. The OR models also performed well in predicting the SP data. The predictive performance of the OR models increased with species rarity and even outperformed the SP model for the rarest species. No significant impact of observer behaviour was found.</p> <p><strong>Main Conclusions:</strong> Relatively simple analyses with inferred absences could produce reliable spatial predictions of habitat suitability. This was especially true for rare species. OR data should be seen as a complement to SP, as the weakness of one is the strength of the other, and OR may be especially useful at large spatial scales or where no systematic data collection protocols exist.</p>
Data from: Life in the cystic fibrosis upper respiratory tract influences competitive ability of the opportunistic pathogen Pseudomonas aeruginosa
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Data from: Playing out Liem's Paradox: opportunistic piscivory across Lake Tanganyikan cichlids
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Data from: Assessing the usefulness of Citizen Science Data for habitat suitability modelling: opportunistic reporting versus sampling based on a systematic protocol
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Data from: Opportunistic data reveal widespread species turnover in Enallagma damselflies at biogeographical scales
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Data from: Tracking two invasions for the cost of one: Opportunistically tracking the range expansion of non-native Palaemon macrodactylus in the Salish Sea through participatory science
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Data from: Patterns of annual and seasonal immune investment in a temporal reproductive opportunist
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Data for: Secondary nectar robbing by Lycaenidae and Riodinidae: opportunistic but not infrequent
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Data from: Broad thermal tolerance is negatively correlated with virulence in an opportunistic bacterial pathogen
Predicting the effects of global increase in temperatures on disease virulence is challenging, especially for environmental opportunistic bacteria, because pathogen fitness may be differentially affected by temperature within and outside host environment. So far, there is very little empirical evidence on the connections between optimal temperature range and virulence in environmentally growing pathogens. Here we explored if the virulence of an environmentally growing opportunistic fish pathogen, Flavobacterium columnare, is malleable to evolutionary changes via correlated selection on thermal tolerance. To this end, we experimentally quantified the thermal performance curves (TPCs) for maximum biomass of 49 F. columnare isolates from eight different geographic locations in Finland over ten years (2003-2012). We also characterized virulence profiles of these strains in a zebra-fish (Danio rerio) infection model. We show that virulence among the strains increased over the years, but tolerance to higher temperatures was negatively associated with virulence. Our data suggest that temperature has a strong effect on the pathogen genetic diversity, and therefore presumably also on disease dynamics. However, the observed increase in frequency and severity of F. columnare epidemics over the last decade cannot be directly linked to bacterial evolution due to increased mean temperature, but is most likely associated with factors related to increased length of growing season, or other time dependent change in environment. Our study demonstrates that complex interactions between the host, the pathogen and the environment influence disease virulence of environmentally growing opportunistic pathogen.
Data from: Tracing the effects of eutrophication on molluscan communities in sediment cores: outbreaks of an opportunistic species coincide with reduced bioturbation and high frequency of hypoxia in the Adriatic Sea
Estimating the effects and timing of anthropogenic impacts on the composition of macrobenthic communities is challenging because early 20th century surveys are sparse and the corresponding intervals in sedimentary sequences are mixed by bioturbation. Here, to assess the effects of eutrophication on macrobenthic communities in the northern Adriatic Sea, we account for mixing with dating of the bivalve Corbula gibba at two stations with high sediment accumulation (Po prodelta) and one station with moderate accumulation (Isonzo prodelta). We find that, first, pervasively bioturbated muds typical of highstand conditions deposited in the early 20th century were replaced by muds with relicts of flood layers and high content of total organic carbon (TOC) deposited in the late 20th century at the Po prodelta. The 20th century shelly muds at the Isonzo prodelta are amalgamated but also show an upward increase in TOC. Second, dating of C. gibba shells shows that the shift from the early to the late 20th century is characterized by a decrease in stratigraphic disorder and by an increase in temporal resolution of death assemblages from ~25-50 years to ~10-20 years in both regions. This shift reflects a decline in the depth of the fully-mixed layer from more than 20 cm to few centimeters. Third, the increase in abundance of the opportunistic species C. gibba and the loss of formerly abundant, hypoxia-sensitive species coincided with the decline in bioturbation, higher preservation of organic matter, and higher frequency of seasonal hypoxia in both regions. This depositional and ecosystem regime shift occurred in ~1950 AD. Therefore, the effects of enhanced food supply on macrobenthic communities were overwhelmed by oxygen depletion even when hypoxic conditions are limited to few weeks per year in the northern Adriatic Sea. Preservation of trends in molluscan abundance and flood events in sedimentary sequences was enhanced by eutrophication that reduced bioturbational mixing.
Data from: Growth and nitrogen uptake characteristics reveal outbreak mechanism of the opportunistic macroalga Gracilaria tenuistipitata
Macroalgae has bloomed in the brackish lake of Shenzhen Bay, China continuously from 2010 to 2014. Gracilaria tenuistipitata was identified as the causative macroalgal species. The aim of this study was to explore the outbreak mechanism of G. tenuistipitata, by studying the effects of salinity and nitrogen sources on growth, and the different nitrogen sources uptake characteristic. Our experimental design was based on environmental conditions observed in the bloom areas, and these main factors were simulated in the laboratory. Results showed that salinity 12 to 20 ‰ was suitable for G. tenuistipitata growth. When the nitrogen sources' (NH4+, NO3−) concentrations reached 40 µM or above, the growth rate of G. tenuistipitata was significantly higher. Algal biomass was higher (approximately 1.4 times) when cultured with NH4+ than that with NO3− addition. Coincidentally, macroalgal bloom formed during times of moderate salinity (~12 ‰) and high nitrogen conditions. The NH4+ and NO3− uptake characteristic was studied to understand the potential mechanism of G. tenuistipitata bloom. NH4+ uptake was best described by a linear, rate-unsaturated response, with the slope decreasing with time intervals. In contrast, NO3− uptake followed a rate-saturating mechanism best described by the Michaelis-Menten model, with kinetic parameters Vmax = 37.2 µM g−1 DM h−1 and Ks = 61.5 µM. Further, based on the isotope 15N tracer method, we found that 15N from NH4+ accumulated faster and reached an atom% twice than that of 15N from NO3−, suggesting when both NH4+ and NO3− were available, NH4+ was assimilated more rapidly. The results of the present study indicate that in the estuarine environment, the combination of moderate salinity with high ammonium may stimulate bloom formation.
Data from: Mapping and explaining wolf recolonization in France using dynamic occupancy models and opportunistic data
While large carnivores are recovering in Europe, assessing their distributions can help to predict and mitigate conflicts with human activities. Because they are highly mobile, elusive and live at very low density, modeling their distributions presents several challenges due to i) their imperfect detectability, ii) their dynamic ranges over time and iii) their monitoring at large scales consisting mainly of opportunistic data without a formal measure of the sampling effort. Here, we focused on wolves (Canis lupus) that have been recolonizing France since the early 90's. We evaluated the sampling effort a posteriori as the number of observers present per year in a cell based on their location and professional activities. We then assessed wolf range dynamics from 1994 to 2016, while accounting for species imperfect detection and time- and space-varying sampling effort using dynamic site-occupancy models. Ignoring the effect of sampling effort on species detectability led to underestimating the number of occupied sites by more than 50% on average. Colonization appeared to be negatively influenced by the proportion of a site with an altitude higher than 2500m and positively influenced by the number of observed occupied sites at short and longdistances , forest cover, farmland cover and mean altitude. The expansion rate, defined as the number of occupied sites in a given year divided by the number of occupied sites in the previous year, decreased over the first years of the study, then remained stable from 2000 to 2016. Our work shows that opportunistic data can be analyzed with species distribution models that control for imperfect detection, pending a quantification of sampling effort. Our approach has the potential for being used by decisionmakers to target sites where large carnivores are likely to occur and mitigate conflicts.
Data from: DNA metabarcoding unveils multi‐scale trophic variation in a widespread coastal opportunist
A thorough understanding of ecological networks relies on comprehensive information on trophic relationships among species. Since unpicking the diet of many organisms is unattainable using traditional morphology‐based approaches, the application of high‐throughput sequencing methods represents a rapid and powerful way forward. Here, we assessed the application of DNA‐metabarcoding with nearly universal primers for the mitochondrial marker cytochrome c oxidase I (COI) in defining the trophic ecology of adult brown shrimp, Crangon crangon, in six European estuaries. The exact trophic role of this abundant and widespread coastal benthic species is somewhat controversial, while information on geographical variation remains scant. Results revealed a highly opportunistic behaviour. Shrimp stomach contents contained hundreds of taxa (>1000 molecular operational taxonomic units), of which 291 were identified as distinct species, belonging to 35 phyla. Only twenty ascertained species had a mean relative abundance of more than 0.5%. Predominant species included other abundant coastal and estuarine taxa, including the shore crab Carcinus maenas and the amphipod Corophium volutator. Jacobs' selectivity index estimates based on DNA extracted from both shrimp stomachs and sediment samples were used to assess the shrimp's trophic niche indicating a generalist diet, dominated by crustaceans, polychaetes and fish. Spatial variation in diet composition, at regional and local scales, confirmed the highly flexible nature of this trophic opportunist. Furthermore, the detection of a prevalent, possibly endoparasitic fungus (Purpureocillium lilacinum) in the shrimp's stomach demonstrates the wide range of questions that can be addressed using metabarcoding, towards a more robust reconstruction of ecological networks.
Data from: Use of opportunistic sightings and expert knowledge to predict and compare Whooping Crane stopover habitat
Predicting a species' distribution can be helpful for evaluating management actions such as critical habitat designations under the U.S. Endangered Species Act or habitat acquisition and rehabilitation. Whooping Cranes (Grus americana) are one of the rarest birds in the world, and conservation and management of habitat is required to ensure their survival. We developed a species distribution model (SDM) that could be used to inform habitat management actions for Whooping Cranes within the state of Nebraska (U.S.A.). We collated 407 opportunistic Whooping Crane group records reported from 1988 to 2012. Most records of Whooping Cranes were contributed by the public; therefore, developing an SDM that accounted for sampling bias was essential because observations at some migration stopover locations may be under represented. An auxiliary data set, required to explore the influence of sampling bias, was derived with expert elicitation. Using our SDM, we compared an intensively managed area in the Central Platte River Valley with the Niobrara National Scenic River in northern Nebraska. Our results suggest, during the peak of migration, Whooping Crane abundance was 262.2 (90% CI 40.2−3144.2) times higher per unit area in the Central Platte River Valley relative to the Niobrara National Scenic River. Although we compared only 2 areas, our model could be used to evaluate any region within the state of Nebraska. Furthermore, our expert-informed modeling approach could be applied to opportunistic presence-only data when sampling bias is a concern and expert knowledge is available.
Data from: Rich resource environment of fish farms facilitates phenotypic variation and virulence in an opportunistic fish pathogen
<p><span>Phenotypic variation is suggested to facilitate the persistence of environmentally growing pathogens under environmental change. Here we hypothesized that the intensive farming environment induces higher phenotypic variation in microbial pathogens than natural environment, because of high stochasticity for growth and stronger survival selection compared to the natural environment. We tested the hypothesis with an opportunistic fish pathogen <em>Flavobacterium columnare</em> isolated either from fish farms or from natural waters. We measured growth parameters of two morphotypes from all isolates in different resource concentrations and two temperatures relevant for the occurrence of disease epidemics at farms and tested their virulence using a zebrafish (<em>Danio rerio</em>) infection model. According to our hypothesis, isolates originating from the fish farms had higher phenotypic variation in growth between the morphotypes than the isolates from natural waters. The difference was more pronounced in higher resource concentrations and the higher temperature, suggesting that phenotypic variation is driven by the exploitation of increased outside-host resources at farms. Phenotypic variation of virulence was not observed based on isolate origin but only based on morphotype. However, when in contact with the larger fish, the less virulent morphotype of some of the isolates also had high virulence. As the less virulent morphotype also had higher growth rate in outside-host resources, the results suggest that both morphotypes can contribute to <em>F. columnare</em> epidemics at fish farms, especially with current prospects of warming temperatures. Our results suggest that higher phenotypic variation per se does not lead to higher virulence, but that environmental conditions at fish farms could select isolates with high phenotypic variation in bacterial population and hence affect evolution in <em>F. columnare</em> at fish farms. Our results highlight the multifaceted effects of human-induced environmental alterations in </span><span>shaping epidemiology and evolution in microbial</span><span> pat</span><span>hogens.</span></p> <p> </p>
Outputs from fitted models across the cross-validation scenarios for 'Space-time species distribution modeling with opportunistic presence-only data: a case study of passerines in a protected area'
<p>Three Zenodo repositories are linked to the preprint <em>Space-time Species Distribution Modeling for Opportunistic Presence-Only Data: A Case Study of Passerines in a Protected Area </em>(Lasgorceux et al., unpublished, <a href="https://hal.science/hal-04616332">https://hal.science/hal-04616332</a>):</p> <ul> <li>Data, scripts and, code (Lasgorceux et al., Zenodo, <a href="https://doi.org/10.5281/zenodo.12545052">https://doi.org/10.5281/zenodo.12545052</a>)</li> <li>Outputs from fitted models across the cross-validation scenarios (Lasgorceux et al., Zenodo, <a href="https://doi.org/10.5281/zenodo.12544212">https://doi.org/10.5281/zenodo.12544212</a>)</li> <li>Supplementary information at (Lasgorceux et al., Zenodo, <a href="https://doi.org/10.5281/zenodo.12541412">https://doi.org/10.5281/zenodo.12541412</a>)</li> </ul> <p>This repository contains the outputs from fitted models across the cross-validation scenarios.</p> <p>In the folder <em>Ouputs_cross_validation</em>, each species is represented by a .RData file, numbered from 1 to 77 (excluding 7, which corresponds to <em>Bombycilla garrulus</em>; see the preprint for details). This dataset is specifically used to generate Figure 1, which shows the AUC of various cross-validation scenarios. To reproduce this figure in R, place all the files in the <em>Results/Fitted_models</em> folder and run the <em>Models_Outputs.R</em> script located in the <em>Results</em> folder of Lasgorceux et al., Zenodo, <a href="https://doi.org/10.5281/zenodo.12545052">https://doi.org/10.5281/zenodo.12545052.</a></p> <p>Note: These data have been separated due to memory requirements (23.14GB).</p>
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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.