Opportunistic Datasets Research Articles

Do Psychopathic Traits, Sexual Victimisation Experiences and Emotional Intelligence Predict Attitudes Towards Rape? Examining the Psychosocial correlates of Rape Myth Beliefs among a cross-sectional community sample

Vast research has sought to better understand the origins and development of rape myth beliefs given the problematic influence of such misconceptions throughout global societies and criminal justice pathways. The current research aims to build on this body of literature by examining the contribution that psychopathic personality traits (affective responsiveness, cognitive responsiveness, interpersonal manipulation, egocentricity) and emotional intelligence may have upon rape myth beliefs. Furthermore, this study will investigate the extent to which sociodemographic characteristics (age, gender, ethnicity, education), and prior experience of sexual victimisation, contribute to variance in rape myth acceptance scores. In total 251 participants (M Age = 31.66) completed an online, self-report questionnaire which included contemporary measures of psychopathy and rape myth acceptance, never previously tested in combination. Results of a hierarchical multiple regression analysis indicate that egocentricity, age, and gender were significantly associated with rape myth beliefs. Emotional intelligence, as well as affective and interpersonal traits of psychopathy, were not directly related with rape mythology. Findings are interpreted alongside previous research, where we suggest there is an urgent need for larger, nationally representative samples, systematically recruited from the general population to help clarify uncertainty in existing literature emerging from small-scale opportunistic datasets.

Turning observations into biodiversity data: Broadscale spatial biases in community science

AbstractBiodiversity community science projects are growing rapidly in popularity. The enormous amounts of data generated by these programs are transforming how we conduct ecological research and conservation management. However, as with other biodiversity surveys, community science datasets suffer from biases in time and locations of observations. To better use these data, we modeled the spatial biases present in the popular community science platform, iNaturalist. iNaturalist uses crowdsourcing to collect georeferenced and time‐stamped observations of all taxa worldwide. With its wealth of biodiversity data, iNaturalist is now being used to answer a broad range of questions in ecology and conservation, but little is known about the platform's spatial biases. We focus on the more than 1.75 million iNaturalist observations available (as of December 2021) from British Columbia, Canada, a region with a strong community science presence and diversity of ecosystems. Using machine learning and species distribution modeling, we examined which landscape factors (e.g., protected areas, roads, human population density, habitat zones, elevation) were most important in determining where observations are taken, and we created a predicted probability map revealing how likely different regions are to be sampled by community scientists. We found strong road biases for observations in iNaturalist, with over 94% of observations within 1 km of roads. In addition, human population density and broad habitat ecosystem zones played a large role in predicting where iNaturalist observations occur across the landscape. These methods demonstrate tools for modeling the effects of spatial biases in large opportunistic datasets that can then be used to produce more accurate species distribution and biodiversity models from community science data.

Fast Bayesian inference for large occupancy datasets.

In recent years, the study of species' occurrence has benefited from the increased availability of large-scale citizen-science data. While abundance data from standardized monitoring schemes are biased toward well-studied taxa and locations, opportunistic data are available for many taxonomic groups, from a large number of locations and across long timescales. Hence, these data provide opportunities to measure species' changes in occurrence, particularly through the use of occupancy models, which account for imperfect detection. These opportunistic datasets can be substantially large, numbering hundreds of thousands of sites, and hence present a challenge from a computational perspective, especially within a Bayesian framework. In this paper, we develop a unifying framework for Bayesian inference in occupancy models that account for both spatial and temporal autocorrelation. We make use of the Pólya-Gamma scheme, which allows for fast inference, and incorporate spatio-temporal random effects using Gaussian processes (GPs), for which we consider two efficient approximations: subset of regressors and nearest neighbor GPs. We apply our model to data on two UK butterfly species, one common and widespread and one rare, using records from the Butterflies for the New Millennium database, producing occupancy indices spanning 45 years. Our framework can be applied to a wide range of taxa, providing measures of variation in species' occurrence, which are used to assess biodiversitychange.

Opportunistic datasets perform poorly in Ecological Niche Modelling: a case study from a polymorphic lizard

Context Among processes involved in colour polymorphism, geographic variation in morph composition and frequency has been attracting interest since it reflects morph local adaptation. A recent study in the Pyrenees associated the pattern of geographic variation in morph frequency of the common wall lizard with the divergence in climatic niches, supporting the hypothesis that morphs represent alternative local climatic adaptations. However, the Pyrenees represent only a small portion of the species range. Aims We modelled the ecological niches of Italian morphs using the same procedure adopted for the Pyrenees to check whether the effects detected at local scales (i.e. the Pyrenees) were repeatable at regional scales (i.e. Italy). This generalisation is needed to investigate how natural selection maintains locally adapted polymorphisms. Methods We classified each locality (120 populations) according to the presence/absence of morphs, and independent Ecological Niche Models (ENMs) against the same background were fitted. Receiver Operating Curves accounting for sampling biases, equivalency and similarity tests were used to check and compare models accounting for spatial distribution of data. Key results Morph-specific ENMs did not reproduce any of the patterns detected in the Pyrenees. Any difference among morphs disappeared after controlling for morph spatial distribution. Since occurrence points of the rarest morphs were a subsample of the occurrence points of the most common morph, it is not possible to separate the effects of true ecological differences among morphs from the effects of the spatial distribution patterns of morph occurrence. Conclusions Using presence data not specifically collected for ENM comparisons does not allow reliable assessments of morph niche segregation. Our analysis points out the need to be very cautious in ecological interpretations of ENMs built on presence/background or presence-only data when occurrences are spatially nested. Implications When dealing with data not specifically collected according to a targeted design, it is not legitimate to compare ENMs with completely nested occurrence points, because this approach can not exclude the possibility that ENM differences were the result of a spatial subsampling. This type of bias is probably largely underestimated, and it may lead to serious misinterpretations as shown in this study.

Drivers of spatio-temporal variation in mosquito submissions to the citizen science project \u2018M\xfcckenatlas\u2019

Intensified travel activities of humans and the ever growing global trade create opportunities of arthropod-borne disease agents and their vectors, such as mosquitoes, to establish in new regions. To update the knowledge of mosquito occurrence and distribution, a national mosquito monitoring programme was initiated in Germany in 2011, which has been complemented by a citizen science project, the ‘Mückenatlas’ since 2012. We analysed the ‘Mückenatlas’ dataset to (1) investigate causes of variation in submission numbers from the start of the project until 2017 and to (2) reveal biases induced by opportunistic data collection. Our results show that the temporal variation of submissions over the years is driven by fluctuating topicality of mosquito-borne diseases in the media and large-scale climate conditions. Hurdle models suggest a positive association of submission numbers with human population, catch location in the former political East Germany and the presence of water bodies, whereas precipitation and wind speed are negative predictors. We conclude that most anthropogenic and environmental effects on submission patterns are associated with the participants’ (recording) behaviour. Understanding how the citizen scientists’ behaviour shape opportunistic datasets help to take full advantage of the available information.

Using Opportunistic Datasets to Infer Spatial Management Strategies of Local Fisheries in the U.S. Caribbean Region

As an initial step towards the implementation of an ecosystem‐based approach to management, the Caribbean Fishery Management Council has recently ratified three island‐based fishery management plans that will supplant the existing regional plans. The newly formed management plans account for nuances among the island platforms in the U.S. Caribbean, including ecological, cultural, and social considerations. The island‐based approach represents a novel strategy among the eight fishery management councils in the United States and therefore remains an untested technique. This study evaluated the decision to separate fishery management among the island platforms by comparing fish assemblages through time in the region. The results suggest that the structure and function of fish communities are spatially driven, with little temporal influence. Combined with the ecological, cultural, and social differences among the islands, the spatial nature of the fish community data support the Caribbean Fishery Management Council decision to transition to island‐based management plans.

Research applications of primary biodiversity databases in the digital age.

Our world is in the midst of unprecedented change—climate shifts and sustained, widespread habitat degradation have led to dramatic declines in biodiversity rivaling historical extinction events. At the same time, new approaches to publishing and integrating previously disconnected data resources promise to help provide the evidence needed for more efficient and effective conservation and management. Stakeholders have invested considerable resources to contribute to online databases of species occurrences. However, estimates suggest that only 10% of biocollections are available in digital form. The biocollections community must therefore continue to promote digitization efforts, which in part requires demonstrating compelling applications of the data. Our overarching goal is therefore to determine trends in use of mobilized species occurrence data since 2010, as online systems have grown and now provide over one billion records. To do this, we characterized 501 papers that use openly accessible biodiversity databases. Our standardized tagging protocol was based on key topics of interest, including: database(s) used, taxa addressed, general uses of data, other data types linked to species occurrence data, and data quality issues addressed. We found that the most common uses of online biodiversity databases have been to estimate species distribution and richness, to outline data compilation and publication, and to assist in developing species checklists or describing new species. Only 69% of papers in our dataset addressed one or more aspects of data quality, which is low considering common errors and biases known to exist in opportunistic datasets. Globally, we find that biodiversity databases are still in the initial stages of data compilation. Novel and integrative applications are restricted to certain taxonomic groups and regions with higher numbers of quality records. Continued data digitization, publication, enhancement, and quality control efforts are necessary to make biodiversity science more efficient and relevant in our fast-changing environment.

The potential for citizen science to produce reliable and useful information in ecology

We examined features of citizen science that influence data quality, inferential power, and usefulness in ecology. As background context for our examination, we considered topics such as ecological sampling (probability based, purposive, opportunistic), linkage between sampling technique and statistical inference (design based, model based), and scientific paradigms (confirmatory, exploratory). We distinguished several types of citizen science investigations, from intensive research with rigorous protocols targeting clearly articulated questions to mass‐participation internet‐based projects with opportunistic data collection lacking sampling design, and examined overarching objectives, design, analysis, volunteer training, and performance. We identified key features that influence data quality: project objectives, design and analysis, and volunteer training and performance. Projects with good designs, trained volunteers, and professional oversight can meet statistical criteria to produce high‐quality data with strong inferential power and therefore are well suited for ecological research objectives. Projects with opportunistic data collection, little or no sampling design, and minimal volunteer training are better suited for general objectives related to public education or data exploration because reliable statistical estimation can be difficult or impossible. In some cases, statistically robust analytical methods, external data, or both may increase the inferential power of certain opportunistically collected data. Ecological management, especially by government agencies, frequently requires data suitable for reliable inference. With standardized protocols, state‐of‐the‐art analytical methods, and well‐supervised programs, citizen science can make valuable contributions to conservation by increasing the scope of species monitoring efforts. Data quality can be improved by adhering to basic principles of data collection and analysis, designing studies to provide the data quality required, and including suitable statistical expertise, thereby strengthening the science aspect of citizen science and enhancing acceptance by the scientific community and decision makers.

Opportunistically collected data reveal habitat selection by migrating Whooping Cranes in the U.S. Northern Plains

ABSTRACT The Whooping Crane (Grus americana) is a federally endangered species in the United States and Canada that relies on wetland, grassland, and cropland habitat during its long migration between wintering grounds in coastal Texas, USA, and breeding sites in Alberta and Northwest Territories, Canada. We combined opportunistic Whooping Crane sightings with landscape data to identify correlates of Whooping Crane occurrence along the migration corridor in North Dakota and South Dakota, USA. Whooping Cranes selected landscapes characterized by diverse wetland communities and upland foraging opportunities. Model performance substantially improved when variables related to detection were included, emphasizing the importance of accounting for biases associated with detection and reporting of birds in opportunistic datasets. We created a predictive map showing relative probability of occurrence across the study region by applying our model to GIS data layers; validation using independent, unbiased locations ...

Virginia and Yellow Rail autumn migration ecology: synthesis using multiple data sets

AbstractVirginia and Yellow Rails are among the least studied birds in North America, and there is a specific lack of information about their autumn migration ecology and migratory habitat use. We conducted nocturnal surveys across 11 public wetlands in Missouri, USA from 2012-2016, and compared the timing of autumn migration from our surveys with three opportunistic datasets: 1) eBird records, 2) building strikes, and 3) state ornithological records. The observed timing (start and end date and duration) of Virginia Rail autumn migration varied between the opportunistic data and our surveys. Virginia Rail opportunistic data were bimodal, while our surveys had a single peak the second week in October. Yellow Rail autumn migration through Missouri peaked earlier in our surveys than opportunistic datasets which peaked during the second week in October. Both rails were found in moist soil habitats, however Virginia Rails selected perennial vegetation more than was available, while Yellow Rails selected annual plant species. Both species showed no selection for water depth and used shallow flooded wetlands. Understanding the autumn migration period and habitat requirements will allow wetland managers to better manage lands for autumn migrating Virginia and Yellow Rails.

Underway sampling of marine inherent optical properties on the Tara Oceans expedition as a novel resource for ocean color satellite data product validation

Developing and validating data records from operational ocean color satellite instruments requires substantial volumes of high quality in situ data. In the absence of broad, institutionally supported field programs, organizations such as the NASA Ocean Biology Processing Group seek opportunistic datasets for use in their operational satellite calibration and validation activities. The publicly available, global biogeochemical dataset collected as part of the two and a half year Tara Oceans expedition provides one such opportunity. We showed how the inline measurements of hyperspectral absorption and attenuation coefficients collected onboard the R/V Tara can be used to evaluate near-surface estimates of chlorophyll-a, spectral particulate backscattering coefficients, particulate organic carbon, and particle size classes derived from the NASA Moderate Resolution Imaging Spectroradiometer onboard Aqua (MODISA). The predominant strength of such flow-through measurements is their sampling rate—the 375 days of measurements resulted in 165 viable MODISA-to-in situ match-ups, compared to 13 from discrete water sampling. While the need to apply bio-optical models to estimate biogeochemical quantities of interest from spectroscopy remains a weakness, we demonstrated how discrete samples can be used in combination with flow-through measurements to create data records of sufficient quality to conduct first order evaluations of satellite-derived data products. Given an emerging agency desire to rapidly evaluate new satellite missions, our results have significant implications on how calibration and validation teams for these missions will be constructed.

A Comparison of Opportunistic Connection Datasets

Opportunistic networking differs from conventional architectures in the lack of existing network infrastructure, which can cause intermittent connectivity or increased communication delay between nodes. From a message routing perspective, solving these problems require a different set of techniques than those used in more traditional network schemes. Forwarding algorithms in these scenarios aim to improve performance metrics such as message delivery ratio and message delay time, while trying to keep the number of message copies small. A common approach used for testing the performance of opportunistic protocols relies on existing opportunistic contact traces. These datasets are widely available on the Internet, and provide a convenient way of simulating realistic usage scenarios. As such, studying the contact patterns between nodes can lead to useful observations to take into account in future experiments. This paper presents the results of a study on four different datasets. First, the authors describe the main characteristics of each trace. Then, they propose a graphical representation of the contact behavior for each pair of nodes. Further analysis of the results in terms of connectivity distribution among nodes reveals that contacts follow a roughly lognormal distribution and that there is a small group of nodes in each set which is seemingly much more popular than the rest. Finally, the authors introduce a temporal analysis that was made over the duration of each collection experiment. It was noticeable that individual nodes have repetitive contact patterns over time, apart from some observed cyclic variation over time (namely on weekends). By modeling the data traces as time-varying graphs, a performance decrease was observed with the absence of the most popular nodes.

Site-occupancy models may offer new opportunities for dragonfly monitoring based on daily species lists

Abstract Monitoring biodiversity is necessary but difficult to achieve in practice, in part because standardized field work is often demanding for volunteer field workers. Collecting opportunistic data on presence and absence of species is much less demanding, but such data may suffer from a number of biases, such as variation in observation effort over time. Here we explore whether site-occupancy models may be helpful to reduce such biases in opportunistic data, especially those caused by temporal variation of observation effort and by incomplete reporting of sightings. Site-occupancy models represent a generalisation of classical metapopulation models to account for imperfect detection; they estimate the probability of sites to be occupied (and of the rates of change, colonisation and extinction rates) while taking into account imperfect detection of a species. The models require so-called presence–absence data from replicated visits for a number of sites (e.g., 20–50). We tested whether these models provide reliable trend estimates if collectors of opportunistic data do not report all species detected. We applied the models to three opportunistic datasets of dragonfly species (1999–2007) in the Netherlands: (1) one-species records, (2) short daily species lists and (3) comprehensive daily species lists. Trend estimates based on a fourth dataset from a standardized monitoring scheme were used as a yardstick to judge the results. The analyses showed that occupancy trends based on comprehensive daily species lists in combination with site-occupancy models were generally similar to those based on the monitoring scheme. But trends based on one-species records and short daily lists were too imprecise to be very useful. In addition, site-occupancy models lead to more realistic occupancy estimates than those obtained from conventional logistic regression analysis. We conclude that comprehensive daily species lists can be useful surrogates for monitoring schemes to assess distributional trends.

Areas and Periods of Culture in the Greater Antilles

Previous articleNext article No AccessAreas and Periods of Culture in the Greater AntillesIrving RouseIrving Rouse Search for more articles by this author PDFPDF PLUS Add to favoritesDownload CitationTrack CitationsPermissionsReprints Share onFacebookTwitterLinkedInRedditEmail SectionsMoreDetailsFiguresReferencesCited by Volume 7, Number 3Autumn, 1951 Article DOIhttps://doi.org/10.1086/soutjanth.7.3.3628603 Views: 8Total views on this site Citations: 17Citations are reported from Crossref Journal History This article was published in the Southwestern Journal of Anthropology (1945-1972), which is continued by the Journal of Anthropological Research (1973-present). PDF download Crossref reports the following articles citing this article:Orian E. Tzadik, William S. Arnold, Mallory Brooks, Juan Jose Cruz-Motta, Graciela Garcia-Moliner, Maria Mar Lopez, Tauna L. Rankin, Alexis M. Sabine, Sarah P. Stephenson Using Opportunistic Datasets to Infer Spatial Management Strategies of Local Fisheries in the U.S. Caribbean Region, Fisheries 46, no.44 (Jan 2021): 156–169.https://doi.org/10.1002/fsh.10548Isaac Shearn Canoe societies in the Caribbean: Ethnography, archaeology, and ecology of precolonial canoe manufacturing and voyaging, Journal of Anthropological Archaeology 57 (Mar 2020): 101140.https://doi.org/10.1016/j.jaa.2019.101140Till Sonnemann, Douglas Comer, Jesse Patsolic, William Megarry, Eduardo Herrera Malatesta, Corinne Hofman Semi-Automatic Detection of Indigenous Settlement Features on Hispaniola through Remote Sensing Data, Geosciences 7, no.44 (Dec 2017): 127.https://doi.org/10.3390/geosciences7040127Andrzej Antczak, Bernardo Urbani, Maria Magdalena Antczak Re-thinking the Migration of Cariban-Speakers from the Middle Orinoco River to North-Central Venezuela (AD 800), Journal of World Prehistory 30, no.22 (May 2017): 131–175.https://doi.org/10.1007/s10963-017-9102-yMonica Barnes, Sumru Aricanli He Perished Ere He Published: Records of the Work of Gary Stockton Vescelius in the American Museum of Natural History, Bulletin of the History of Archaeology 26, no.11 (Dec 2016).https://doi.org/10.5334/bha-588William J. Pestle, L. Antonio Curet, Reniel Rodríguez Ramos, Miguel Rodríguez López New Questions and Old Paradigms: Reexamining Caribbean Culture History, Latin American Antiquity 24, no.33 (Jan 2017): 243–261.https://doi.org/10.7183/1045-6635.24.3.243William H. Sears, Shaun O. Sullivan Bahamas Prehistory, American Antiquity 43, no.11 (Jan 2017): 3–25.https://doi.org/10.2307/279627Irving Rouse Pattern and process in West Indian archaeology, World Archaeology 9, no.11 (Jul 2010): 1–11.https://doi.org/10.1080/00438243.1977.9979681Ricardo E. Alegría On Puerto Rican Archaeology, American Antiquity 31, no.2Part12Part1 (Jan 2017): 246–249.https://doi.org/10.2307/2693992Robert R. Howard New Perspectives on Jamaican Archaeology, American Antiquity 31, no.2Part12Part1 (Jan 2017): 250–255.https://doi.org/10.2307/2693993Irving Rouse Prehistory of the West Indies, Science 144, no.36183618 (May 1964): 499–513.https://doi.org/10.1126/science.144.3618.499Monica Flaherty Frassetto A Preliminary Report on Petroglyphs in Puerto Rico, American Antiquity 25, no.33 (Jan 2017): 381–391.https://doi.org/10.2307/277521Edward S. Deevey, L. J. Gralenski, Väinö Hoffren Yale Natural Radiocarbon Measurements IV, Radiocarbon 1 (Jul 2016): 144–172.https://doi.org/10.1017/S0033822200020440 Douglas Taylor , and Irving Rouse Linguistic and Archeological Time Depth in the West Indies, International Journal of American Linguistics 21, no.22 (Sep 2015): 105–115.https://doi.org/10.1086/464319Gordon R. Willey, Charles C. Di Peso, William A. Ritchie, Irving Rouse, John H. Rowe, Donald W. Lathrap An Archaeological Classification of Culture Contact Situations, Memoirs of the Society for American Archaeology 11 (Jun 2018): 2–30.https://doi.org/10.1017/S0081130000001441Irving Rouse On the Use of the Concept of Area Co-Tradition, American Antiquity 19, no.33 (Nov 2021): 221–225.https://doi.org/10.2307/277127Irving Rouse The Circum-Caribbean Theory, An Archeological Test, American Anthropologist 55, no.22 (Oct 2009): 188–200.https://doi.org/10.1525/aa.1953.55.2.02a00040