scholarly journals Aggregated occurrence records of the federally endangered Poweshiek skipperling (Oarisma poweshiek)

2018 ◽  
Vol 6 ◽  
Author(s):  
Michael Belitz ◽  
Lillian Hendrick ◽  
Michael Monfils ◽  
David Cuthrell ◽  
Christopher Marshall ◽  
...  
Keyword(s):  
Natural History ◽  
Uncertain Data ◽  
Population Monitoring ◽  
Original Data ◽  
Common Species ◽  
Controlled Vocabulary ◽  
Occurrence Data ◽  
Presence Data ◽  
Conservation Projects ◽  
Occurrence Records

Primary biodiversity data records that are open access and available in a standardised format are essential for conservation planning and research on policy-relevant time-scales. We created a dataset to document all known occurrence data for the Federally Endangered Poweshiek skipperling butterfly [Oarismapoweshiek (Parker, 1870; Lepidoptera: Hesperiidae)]. The Poweshiek skipperling was a historically common species in prairie systems across the upper Midwest, United States and Manitoba, Canada. Rapid declines have reduced the number of verified extant sites to six. Aggregating and curating Poweshiek skipperling occurrence records documents and preserves all known distributional data, which can be used to address questions related to Poweshiek skipperling conservation, ecology and biogeography. Over 3500 occurrence records were aggregated over a temporal coverage from 1872 to present. Occurrence records were obtained from 37 data providers in the conservation and natural history collection community using both “HumanObservation” and “PreservedSpecimen” as an acceptable basisOfRecord. Data were obtained in different formats and with differing degrees of quality control. During the data aggregation and cleaning process, we transcribed specimen label data, georeferenced occurrences, adopted a controlled vocabulary, removed duplicates and standardised formatting. We examined the dataset for inconsistencies with known Poweshiek skipperling biogeography and phenology and we verified or removed inconsistencies by working with the original data providers. In total, 12 occurrence records were removed because we identified them to be the western congener Oarismagarita (Reakirt, 1866). This resulting dataset enhances the permanency of Poweshiek skipperling occurrence data in a standardised format. This is a validated and comprehensive dataset of occurrence records for the Poweshiek skipperling (Oarismapoweshiek) utilising both observation and specimen-based records. Occurrence data are preserved and available for continued research and conservation projects using standardised Darwin Core formatting where possible. Prior to this project, much of these occurrence records were not mobilised and were being stored in individual institutional databases, researcher datasets and personal records. This dataset aggregates presence data from state conservation agencies, natural heritage programmes, natural history collections, citizen scientists, researchers and the U.S. Fish & Wildlife Service. The data include opportunistic observations and collections, research vouchers, observations collected for population monitoring and observations collected using standardised research methodologies. The aggregated occurrence records underwent cleaning efforts that improved data interoperablitity, removed transcription errors and verified or removed uncertain data. This dataset enhances available information on the spatiotemporal distribution of this Federally Endangered species. As part of this aggregation process, we discovered and verified Poweshiek skipperling occurrence records from two previously unknown states, Nebraska and Ohio.

A rangewide distribution model for the Pallas’s cat (Otocolobus manul): identifying potential new survey regions for an understudied small cat

Mammalia ◽  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Evan Greenspan ◽  
Anthony J. Giordano
Keyword(s):  
Prey Species ◽  
Distribution Model ◽  
Research Attention ◽  
Potential Habitat ◽  
Kunlun Mountains ◽  
Presence Data ◽  
Pamir Mountains ◽  
Conservation Actions ◽  
Regional Conservation ◽  
Occurrence Records

Abstract Knowledge about the current distribution of threatened and/or understudied species is a fundamental component of conservation biology. Mapping species distributions based on recent known occurrences is particularly important for those that are rare or declining. Too often, cryptic species go undetected throughout parts of their range, whereas others just receive less research attention. We used contemporary presence data for the Pallas’s cat (Otocolobus manul), a small cryptic felid, to characterize potential rangewide and regional habitat for the species and identify those abiotic and biotic variables most influencing its distribution. Several regions lacking contemporary occurrence records contain potential habitat for Pallas’s cats, including the Koh-i-Baba Mountains of Afghanistan, Qinghai-Tibetan Plateau, steppes of Inner Mongolia, Kunlun Mountains of China, and Tian Shan and Pamir Mountains of Kyrgyzstan, Tajikistan, and China. Some of these areas have not been included in prior rangewide distribution assessments. The distribution of pikas (Ochotona spp.), small mammals that likely represent a critical prey species everywhere they are sympatric, was the most important factor affecting the Pallas’s cat’s distribution. This suggests Pallas’s cats may be prey specialists, and that pika presence and habitat are critical considerations for future Pallas’s cat surveys and in the development of regional conservation actions.

scholarly journals Addressing Uncertainties in Machine Learning Predictions of Conservation Status

2019 ◽  
Vol 3 ◽  
Author(s):  
Barnaby Walker ◽  
Tarciso Leão ◽  
Steven Bachman ◽  
Eve Lucas ◽  
Eimear Nic Lughadha
Keyword(s):  
Machine Learning ◽  
Natural History ◽  
Extinction Risk ◽  
Conservation Status ◽  
Red List ◽  
Learning Models ◽  
Species Occurrence ◽  
Voucher Specimens ◽  
Machine Learning Models ◽  
Occurrence Records

Extinction risk assessments are increasingly important to many stakeholders (Bennun et al. 2017) but there remain large gaps in our knowledge about the status of many species. The IUCN Red List of Threatened Species (IUCN 2019, hereafter Red List) is the most comprehensive assessment of extinction risk. However, it includes assessments of just 7% of all vascular plants, while 18% of all assessed animals lack sufficient data to assign a conservation status. The wide availability of species occurrence information through digitised natural history collections and aggregators such as the Global Biodiversity Information Facility (GBIF), coupled with machine learning methods, provides an opportunity to fill these gaps in our knowledge. Machine learning approaches have already been proposed to guide conservation assessment efforts (Nic Lughadha et al. 2018), assign a conservation status to species with insufficient data for a full assessment (Bland et al. 2014), and predict the number of threatened species across the world (Pelletier et al. 2018). The wide range in sources of species occurrence records can lead to data quality issues, such as missing, imprecise, or mistaken information. These data quality issues may be compounded in databases that aggregate information from multiple sources: many such records derive from field observations (78% for plant species in GBIF; Meyer et al. 2016) largely unsupported by voucher specimens that would allow confirmation or correction of their identification. Even where voucher specimens do exist, different taxonomic or geographic information can be held for a single collection event represented by duplicate specimens deposited in different natural history collections. Tools are available to help clean species occurrence data, but these cannot deal with problems like specimen misidentification, which previous work (Nic Lughadha et al. 2019) has shown to have a large impact on preliminary assessments of conservation status. Machine learning models based on species occurrence records have been reported to predict with high accuracy the conservation status of species. However, given the black-box nature of some of the better machine learning models, it is unclear how well these accuracies apply beyond the data on which the models were trained. Practices for training machine learning models differ between studies, but more interrogation of these models is required if we are to know how much to trust their predictions. To address these problems, we compare predictions made by a machine learning model when trained on specimen occurrence records that have benefitted from minimal or more thorough cleaning, with those based on records from an expert-curated database. We then explore different techniques to interrogate machine learning models and quantify the uncertainty in their predictions.

The natural history of the Stenoderminae Chiroderma doriae vizottoi Taddei and Lim 2010 (Chiroptera, Phyllostomidae) in a semiarid region from Brazil

Mammalia ◽  
2022 ◽  
Vol 0 (0) ◽  
Author(s):  
Isabela Silva Bellizzi ◽  
Shirley Seixas Pereira da Silva ◽  
Patrícia Gonçalves Guedes ◽  
Juliana Cardoso de Almeida
Keyword(s):  
Gastrointestinal Tract ◽  
Natural History ◽  
Body Length ◽  
Rainy Season ◽  
Original Data ◽  
The State ◽  
Semiarid Region ◽  
Internal Anatomy ◽  
Internal Organs ◽  
History Of

Abstract Original data on diet, internal anatomy, morphology, reproduction, and parasites of Chiroderma doriae vizottoi from the State of Ceará (Brazil) are presented. Intact and crushed seeds of Solanum rhytidoandrum and scales of Lepidoptera were detected in the gastrointestinal tract and feces. Observation of internal organs did not reveal any abnormalities; the intestines were, on average, 11 times longer than the animal’s body length. Reproduction seems to occur in the rainy season. The association with an ectoparasite, Mastoptera sp. (Diptera, Streblidae), was recorded.

scholarly journals Natural history of Ctenus medius Keyserling, 1891 (Araneae, Ctenidae) I: observations on habitats and the development of chromatic patterns

2000 ◽  
Vol 60 (3) ◽  
pp. 503-509 ◽  
Author(s):  
C. E. ALMEIDA ◽  
E. F. RAMOS ◽  
E. GOUVÊA ◽  
M. do CARMO-SILVA ◽  
J. COSTA
Keyword(s):  
Life Cycle ◽  
Natural History ◽  
National Park ◽  
Common Species ◽  
Field Observations ◽  
Mata Atlântica ◽  
Adult Females ◽  
History Of ◽  
Males And Females ◽  
Phoneutria Nigriventer

Ctenus medius Keyserling, 1891 is a common species in several spots of Mata Atlântica, however there is a great lack of studies in all aspects of its natural history. This work aims to elucidate aspects of ecotope preference compared to large spiders, and to provide data on the development of chromatic patterns during its life cycle. The observations on the behavior of C. medius were done in the campus of Centro Universitário de Barra Mansa (UBM) by means of observations and nocturnal collections using cap lamps. For observations on the development of chromatic patterns, spiderlings raised in laboratory, hatched from an oviposition of a female from campus of UBM, and others spiderlings collected in field were used. The field observations indicate that: C. medius seems to prefer ecotopes characterized by dense shrub vegetation or herbal undergrowth; Lycosa erythrognatha and L. nordeskioldii seems to prefer open sites; Phoneutria nigriventer seems to prefer shrub vegetation and anthropogenic ecotopes as rubbish hills; Ancylometes sp. seems to prefer ecotopes near streams. Concerning chromatic patterns, it was observed that males and females show well distinct patterns during the last two instars, allowing distinction by sex without the use of a microscope. Through chromatic patterns it was also possible to draw a distinction between C. medius and C. ornatus longer that 3 mm cephalothorax width. 69 specimens of C. medius (males and females) collected in the campus of UBM did not show a striking polymorphism in chromatic pattern, but one among 7 adult females collected in National Park of Itatiaia, showed a distinct chromatic pattern.

Clergymen Abiding in the Fields: The Making of the Naturalist Observer in Eighteenth-Century Norwegian Natural History

2011 ◽  
Vol 24 (2) ◽  
pp. 143-166 ◽  
Author(s):  
Brita Brenna
Keyword(s):  
Eighteenth Century ◽  
Natural History ◽  
Common Species ◽  
Natural Phenomena ◽  
Church Organization ◽  
The North ◽  
Self Sufficiency ◽  
Form Of Government ◽  
The Government ◽  
First Time

ArgumentBy the mid-eighteenth century, governors of the major European states promoted the study of nature as part of natural-resource based schemes for improvement and economic self-sufficiency. Procuring beneficial knowledge about nature, however, required observers, collectors, and compilers who could produce usable and useful descriptions of nature. The ways governments promoted scientific explorations varied according to the form of government, the makeup of the civil society, the state's economic ideologies and practices, and the geographical situation. This article argues that the roots of a major natural history initiative in Denmark-Norway were firmly planted in the state-church organization. Through the clergymen and their activities, a bishop, supported by the government in Copenhagen, could gather an impressive collection of natural objects, receive observations and descriptions of natural phenomena, and produce natural historical publications that described for the first time many of the species of the north. Devout naturalists were a common species in the eighteenth century, when clergymen and missionaries involved themselves in the investigation of nature in Europe and far beyond. The specific interest here is in how natural history was supported and enforced as part of clerical practice, how specimen exchange was grafted on to pre-existing institutions of gift exchange, and how this influenced the character of the knowledge produced.

scholarly journals occAssess: An R package for assessing potential biases in species occurrence data

2021 ◽  
Author(s):  
Robin James Boyd ◽  
Gary Powney ◽  
Claire Carvell ◽  
Oliver Pescott
Keyword(s):  
Target Population ◽  
R Package ◽  
Heterogeneous Databases ◽  
Ecological Research ◽  
Species Occurrence ◽  
Worked Example ◽  
Occurrence Data ◽  
Data Coverage ◽  
Discrete Functions ◽  
Occurrence Records

Species occurrence records from a variety of sources are increasingly aggregated into heterogeneous databases and made available to ecologists for immediate analytical use. However, these data are typically biased, i.e. they are not a representative sample of the target population of interest, meaning that the information they provide may not be an accurate reflection of reality. It is therefore crucial that species occurrence data are properly scrutinised before they are used for research. In this article, we introduce occAssess, an R package that enables quick and easy screening of species occurrence data for potential biases. The package contains a number of discrete functions, each of which returns a measure of the potential for bias in one or more of the taxonomic, temporal, spatial and environmental dimensions. The outputs are provided visually (as ggplot2 objects) and do not include a formal recommendation as to whether data are of sufficient quality for any given inferential use. Instead, they should be used as ancillary information and viewed in the context of the question that is being asked, and the methods that are being used to answer it. We demonstrate the utility of occAssess by applying it to data on two key pollinator taxa in South America: leaf-nosed bats (Phyllostomidae) and hoverflies (Syrphidae). In this worked example, we briefly assess the degree to which various aspect of data coverage appear to have changed over time. We then discuss additional ways in which the package could be used, highlight its limitations, and point to where it could be improved in the future. Going forward, we hope that occAssess will help to improve the quality, and transparency, of assessments of species occurrence data as a necessary first step where they are being used for ecological research at large scales.

scholarly journals Exploring snake occurrence records: Spatial biases and marginal gains from accessible social media

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e8059 ◽  
Author(s):  
Benjamin M. Marshall ◽  
Colin T. Strine
Keyword(s):  
Social Media ◽  
North America ◽  
South America ◽  
Species Distribution ◽  
Species Distribution Models ◽  
Conservation Status ◽  
Model Performance ◽  
Distribution Models ◽  
Occurrence Data ◽  
Occurrence Records

A species’ distribution provides fundamental information on: climatic niche, biogeography, and conservation status. Species distribution models often use occurrence records from biodiversity databases, subject to spatial and taxonomic biases. Deficiencies in occurrence data can lead to incomplete species distribution estimates. We can incorporate other data sources to supplement occurrence datasets. The general public is creating (via GPS-enabled cameras to photograph wildlife) incidental occurrence records that may present an opportunity to improve species distribution models. We investigated (1) occurrence data of a cryptic group of animals: non-marine snakes, in a biodiversity database (Global Biodiversity Information Facility (GBIF)) and determined (2) whether incidental occurrence records extracted from geo-tagged social media images (Flickr) could improve distribution models for 18 tropical snake species. We provide R code to search for and extract data from images using Flickr’s API. We show the biodiversity database’s 302,386 records disproportionately originate from North America, Europe and Oceania (250,063, 82.7%), with substantial gaps in tropical areas that host the highest snake diversity. North America, Europe and Oceania averaged several hundred records per species; whereas Asia, Africa and South America averaged less than 35 per species. Occurrence density showed similar patterns; Asia, Africa and South America have roughly ten-fold fewer records per 100 km2than other regions. Social media provided 44,687 potential records. However, including them in distribution models only marginally impacted niche estimations; niche overlap indices were consistently over 0.9. Similarly, we show negligible differences in Maxent model performance between models trained using GBIF-only and Flickr-supplemented datasets. Model performance appeared dependent on species, rather than number of occurrences or training dataset. We suggest that for tropical snakes, accessible social media currently fails to deliver appreciable benefits for estimating species distributions; but due to the variation between species and the rapid growth in social media data, may still be worth considering in future contexts.

scholarly journals Sampling bias in reptile occurrence data for the Kruger National Park

Koedoe ◽  
2020 ◽  
Vol 62 (1) ◽  
Author(s):  
Jody M. Barends ◽  
Darren W. Pietersen ◽  
Guinevere Zambatis ◽  
Donovan R.C. Tye ◽  
Bryan Maritz
Keyword(s):  
National Park ◽  
Conservation Management ◽  
Species Distributions ◽  
Sampling Bias ◽  
Kruger National Park ◽  
Data Set ◽  
Occurrence Data ◽  
Reptile Species ◽  
Sampling Biases ◽  
Occurrence Records

o effectively conserve and manage species, it is important to (1) understand how they are spatially distributed across the globe at both broad and fine spatial resolutions and (2) elucidate the determinants of these distributions. However, information pertaining to the distributions of many species remains poor as occurrence data are often scarce or collected with varying motivations, making the resulting patterns susceptible to sampling bias. Exacerbating an already limited quantity of occurrence data with an assortment of biases hinders their effectiveness for research, thus making it important to identify and understand the biases present within species occurrence data sets. We quantitatively assessed occurrence records of 126 reptile species occurring in the Kruger National Park (KNP), South Africa, to quantify the severity of sampling bias within this data set. We collated a data set of 7118 occurrence records from museum, literature and citizen science sources and analysed these at a biologically relevant spatial resolution of 1 km × 1 km. As a result of logistical challenges associated with sampling in KNP, approximately 92% of KNP is data deficient for reptile occurrences at the 1 km × 1 km resolution. Additionally, the spatial coverage of available occurrences varied at species and family levels, and the majority of occurrence records were strongly associated with publicly accessible human infrastructure. Furthermore, we found that sampled areas within KNP were not necessarily ecologically representative of KNP as a whole, suggesting that areas of unique environmental space remain to be sampled. Our findings highlight the need for substantially greater sampling effort for reptiles across KNP and emphasise the need to carefully consider the sampling biases within existing data should these be used for conservation management decision-making. Modelling species distributions could potentially serve as a short-term solution, but a concomitant increase in surveys across the park is needed.Conservation implications: The sampling biases present within KNP reptile occurrence data inhibit the inference of fine-scale species distributions within and across the park, which limits the usage of these data towards meaningfully informing conservation management decisions as applicable to reptile species in KNP.

Using occurrence data to evaluate extinction reveals a strong resilience of butterflies in a National Park of Southern Europe (Alta Murgia National Park)

2021 ◽  
Vol 28 (1) ◽  
Author(s):  
ROCCO LABADESSA ◽  
GIUSEPPE CAGNETTA ◽  
JEAN-FRANCOIS DESAPHY ◽  
MARCO BONIFACINO ◽  
GIUSEPPE DODARO ◽  
...  
Keyword(s):  
National Park ◽  
Extinction Risk ◽  
Large Fraction ◽  
Heterogeneous Environments ◽  
European Regions ◽  
Conservation Area ◽  
Occurrence Data ◽  
Sample Data ◽  
Butterfly Community ◽  
Occurrence Records

Butterflies from southernmost European regions encompass a large fraction of faunistic and genetic diversity but are also at the forefront of extinction risk for climate change. Nevertheless, monitoring schemes aimed at detecting their population trends were only recently established. In this study, we gathered all occurrence records of the 81 species of butterflies recorded for the Alta Murgia National Park (Italy, Apulia), a prime conservation area for butterflies. By using literature, citizen science, and unpublished sample data, we traced potential extinctions since 1966. We also provided a dedicated index to evaluate the potential extinction at the whole community level. We found that among the 29 species recorded before 2009, three were not recovered from 2009 to 2021. Another group of nine species was not recorded in the last five years. However, given the not standardized sampling methodology and the possibility that apparently disappeared species were due to inaccurate identification, we conclude that the butterfly community of the Park is showing a strong resilience. We hypothesize that such resilience may be attributed to the existence of the protected area and the presence of heterogeneous environments, which allow to buffer climatic changes and any other negative anthropic effects. The objective recognition of rare species in the surrounding region of 200 km ray also allowed identifying which species should be considered as prime targets for the conservation of local and regional diversity.

scholarly journals Text-mining BHL: towards new interfaces to the biodiversity literature

2019 ◽  
Vol 3 ◽  
Author(s):  
Roderic Page
Keyword(s):  
Text Mining ◽  
Reverse Direction ◽  
Simple Method ◽  
Biodiversity Knowledge ◽  
Digital Representations ◽  
Occurrence Data ◽  
Scientific Disciplines ◽  
International Image ◽  
Locality Information ◽  
Occurrence Records

The taxonomic literature is one of the largest resources of information on biodiversity, both current and in the past. Unlike many scientific disciplines this literature remains perpetually relevant as successive taxonomic work builds upon those earlier foundations. Projects such as the Biodiversity Heritage Library (BHL) have greatly increased access to that literature, as have numerous independent digitisation efforts by museums, herbaria, and publishers. But the focus of this access has been human readers, with limited use of text mining tools, mostly focussed on extracting taxonomic names. This talk explores other kinds of data that can be extracted from text on BHL and elsewhere, focusing on taxonomic names, geographic localities and specimen codes in the context of the BioStor project (https://biostor.org, Page 2011). The problem of finding taxonomic names in text has been well studied (e.g., Akella et al. 2012), and new BHL content is continuously indexed by names. Despite this, there is only weak linkage between taxonomic name databases and BHL. Even projects that create these links (e.g., BioNames, Page 2013) do not enable links in the reverse direction. In other words, a BHL reader is unaware whether the appearance of a name on a page is the first publication of that name, nor are they told of the fate of a name in subsequent research. The absence of these links reduces the value of BHL to working taxonomists. In addition to taxonomic names, a typical taxonomic paper often contains specimen codes. Extracting these from text and linking them to digital representations, such as occurrence records in GBIF, opens up the possibility to provide detailed provenance for occurrence data, as well as citation-based metrics for the utility of natural history collections. Taxonomic papers are also often rich in geographic information. A simple method for extracting locality information from text is to search for latitude and longitude coordinates, and BioStor currently does this. To date some 83,000 individual point localities have been extracted (Fig. 1 ). These are used to provide a simple geographic search interface in BioStor, and are also harvested by JournalMap (Karl et al. 2013). But these localities are not linked to the original location in the source text, nor are they linked to any associated specimens, so they cannot be interpreted as occurrences that could be harvested by GBIF. If the goal is to contribute to GBIF then we need tools that can parse locality information and link that to associated specimens. A general framework for handling data on taxonomic names, specimens, and geographic localities in text is to treat them as annotations (Batista-Navarro et al. 2017). By modelling annotations using the Web Annotation Data Model (https://www.w3.org/TR/annotation-model/ ) we can incorporate these annotations into biodiversity knowledge graphs (Page 2016). We can also combine these annotations with new standards for describing digitised content, such as the International Image Interoperability Framework (IIIF, https://iiif.io). The implications of this approach for developing new interfaces to the biodiversity literature will be discussed.

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