Multidimensional biases, gaps and uncertainties in global plant occurrence information

10.7287/peerj.preprints.1326v1 ◽

2015 ◽

Author(s):

Carsten Meyer ◽

Holger Kreft ◽

Patrick Weigelt

Keyword(s):

Conceptual Framework ◽

Conservation Status ◽

Multidimensional Data ◽

Trade Offs ◽

Data Limitations ◽

Other Information ◽

Temporal Dimensions ◽

Plant Ecological ◽

Information Metrics ◽

Occurrence Records

Plants are a hyperdiverse clade that plays a key role in maintaining ecological and evolutionary processes as well as human livelihoods. Glaring biases, gaps, and uncertainties in plant occurrence information remain a central problem in ecology and conservation, but these limitations have never been assessed globally. In this synthesis, we propose a conceptual framework for analyzing information biases, gaps and uncertainties along taxonomic, geographical, and temporal dimensions and apply it to all c. 370,000 species of land plants. To this end, we integrated 120 million point-occurrence records with independent databases on plant taxonomy, distributions, and conservation status. We find that different data limitations are prevalent in each dimension. Different information metrics are largely uncorrelated, and filtering out specific limitations would usually lead to extreme trade-offs for other information metrics. In light of these multidimensional data limitations, we critically discuss prospects for global plant ecological and biogeographical research, monitoring and conservation, and outline critical next steps towards more effective information usage and mobilization. We provide an empirical baseline for evaluating and improving global floristic knowledge and our conceptual framework can be applied to the study of other hyperdiverse clades.

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Multidimensional biases, gaps and uncertainties in global plant occurrence information

10.7287/peerj.preprints.1326v2 ◽

2015 ◽

Author(s):

Carsten Meyer ◽

Patrick Weigelt ◽

Holger Kreft

Keyword(s):

Conceptual Framework ◽

Conservation Status ◽

Multidimensional Data ◽

Trade Offs ◽

Data Limitations ◽

Other Information ◽

Temporal Dimensions ◽

Plant Ecological ◽

Information Metrics ◽

Occurrence Records

Plants are a hyperdiverse clade that plays a key role in maintaining ecological and evolutionary processes as well as human livelihoods. Glaring biases, gaps, and uncertainties in plant occurrence information remain a central problem in ecology and conservation, but these limitations have never been assessed globally. In this synthesis, we propose a conceptual framework for analyzing information biases, gaps and uncertainties along taxonomic, geographical, and temporal dimensions and apply it to all c. 370,000 species of land plants. To this end, we integrated 120 million point-occurrence records with independent databases on plant taxonomy, distributions, and conservation status. We find that different data limitations are prevalent in each dimension. Different information metrics are largely uncorrelated, and filtering out specific limitations would usually lead to extreme trade-offs for other information metrics. In light of these multidimensional data limitations, we critically discuss prospects for global plant ecological and biogeographical research, monitoring and conservation, and outline critical next steps towards more effective information usage and mobilization. We provide an empirical baseline for evaluating and improving global floristic knowledge and our conceptual framework can be applied to the study of other hyperdiverse clades.

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Lessons learned from data-limited evaluations of data-rich reef fish species in the Gulf of Mexico: implications for providing fisheries management advice for data-poor stocks

Canadian Journal of Fisheries and Aquatic Sciences ◽

10.1139/cjfas-2017-0482 ◽

2019 ◽

Vol 76 (9) ◽

pp. 1624-1639 ◽

Cited By ~ 3

Author(s):

Skyler R. Sagarese ◽

William J. Harford ◽

John F. Walter ◽

Meaghan D. Bryan ◽

J. Jeffery Isely ◽

...

Keyword(s):

Gulf Of Mexico ◽

Reef Fish ◽

Fish Species ◽

Stock Assessment ◽

Lessons Learned ◽

Management Objectives ◽

Trade Offs ◽

Data Limitations ◽

Management Procedures ◽

Reef Fish Species

Specifying annual catch limits for artisanal fisheries, low economic value stocks, or bycatch species is problematic due to data limitations. Many empirical management procedures (MPs) have been developed that provide catch advice based on achieving a stable catch or a historical target (i.e., instead of maximum sustainable yield). However, a thorough comparison of derived yield streams between empirical MPs and stock assessment models has not been explored. We first evaluate trade-offs in conservation and yield metrics for data-limited approaches through management strategy evaluation (MSE) of seven data-rich reef fish species in the Gulf of Mexico. We then apply data-limited approaches for each species and compare how catch advice differs from current age-based assessment models. MSEs identified empirical MPs (e.g., using relative abundance) as a compromise between data requirements and the ability to consistently achieve management objectives (e.g., prevent overfishing). Catch advice differed greatly among data-limited approaches and current assessments, likely due to data inputs and assumptions. Adaptive MPs become clearly viable options that can achieve management objectives while incorporating auxiliary data beyond catch-only approaches.

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Addressing Uncertainties in Machine Learning Predictions of Conservation Status

Biodiversity Information Science and Standards ◽

10.3897/biss.3.37147 ◽

2019 ◽

Vol 3 ◽

Cited By ~ 1

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.

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The questionnaire on productivity attributes (QPA)

International Journal of Productivity and Performance Management ◽

10.1108/ijppm-01-2017-0012 ◽

2018 ◽

Vol 67 (5) ◽

pp. 845-872

Author(s):

Raju Sheshrao Kamble ◽

Lalit Narendra Wankhade

Keyword(s):

Conceptual Framework ◽

Ordinal Data ◽

Goodness Of Fit ◽

Basic Research ◽

Manufacturing Industries ◽

General Measure ◽

Goodness Of Fit Test ◽

Productivity Improvement ◽

Content Type ◽

Trade Offs

PurposeAlthough there are many studies investigating attributes affecting productivity, the research into measurement of those attributes has been incomplete. In an attempt to bridge this gap, the authors reviewed the productivity literature, identified and integrated previously described attributes, and developed a measure to estimate those attributes. The developed questionnaire – questionnaire on productivity attributes (QPA) – is based on a five-dimensional conceptual framework, which consisted of human resource management, management strategy, organizational culture, production methodology, and performance. A model that measures a way to construct a linear scale from ordinal data has also been introduced. The paper aims to discuss these issues.Design/methodology/approachInitially, a pilot survey among Indian academic and industrial experts as well as employees working in manufacturing industries was conducted to optimize clarity, readability, and construction of the QPA. After pilot-testing, the 45 QPA items were further field surveyed amongst a representative sample of 311 Indian engineers, managers, and workers from manufacturing industries. One-way analysis of variance is performed to examine whether there are differences among engineers, managers, and workers in the understandability or applicability of QPA. Exploratory factor analysis is used to confirm the five-dimensional conceptual framework. Also, infit and outfit measures have been used to check the QPA model fit. To increase confidence, all retained items are tested for goodness-of-fit test. Finally, the functioning of optimal response categorization of the QPA is demonstrated in terms of frequencies, average measures, and standard error.FindingsA five-dimensional conceptual framework is identified. A generic short scale was constructed. Finally, the developed questionnaire provides new insights into how to avoid the trade-offs commonly observed in productivity research.Originality/valueThe newly designed QPA appears as a general measure for productivity attributes which can be used by scholars and practitioners to conduct basic research on productivity improvement in various industries.

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An Empirical Study of the use of EDI in Supermarket Chains using a New Conceptual Framework

Journal of Information Technology ◽

10.1177/026839629901400102 ◽

1999 ◽

Vol 14 (1) ◽

pp. 3-21 ◽

Cited By ~ 2

Author(s):

Colm Fearon ◽

George Philip

Keyword(s):

Conceptual Framework ◽

Electronic Data Interchange ◽

Medium Size ◽

Senior Managers ◽

Self Assessment ◽

Data Interchange ◽

Electronic Transactions ◽

Other Information ◽

Expected Benefits ◽

Almost All

Electronic Data Interchange (EDI) is now widely established as an interorganizational system in almost all large and medium size industrial, service and retail sectors and the purpose of this paper is to discuss the experiences of six supermarket chains concerning the use of this technology. Whilst the advantages of conducting electronic transactions are generally recognized, consensus on methodologies for quantifying the benefit success associated with interorganizational systems still remain elusive. It is argued that a comparison of pre-implementation expected benefits with post-implementation realized benefits can offer a reliable way of assessing the benefit success from EDI and other information systems. In this approach, the benefit success is expressed as a function of three benefit states namely, efficiency, neutrality and deficiency depending on whether the gap between realized and expected benefits is positive, neutral or negative respectively. In order to identify the benefit state of each participating organization, initially a series of semistructured interviews was conducted with senior managers followed by the administration of a self assessment rating instrument in the form of a questionnaire. This paper will discuss the findings along with the testing of a new conceptual framework for examining the relationship between benefit success, implementation approach and implementation success.

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Miconia gigantea, a long-forgotten endemic and endangered species of Melastomataceae in the Brazilian Atlantic Forest

Anais da Academia Brasileira de Ciências ◽

10.1590/0001-3765201620150612 ◽

2016 ◽

Vol 88 (3 suppl) ◽

pp. 1809-1818

Author(s):

LILIAN P.G. DA ROSA ◽

JOSÉ F.A. BAUMGRATZ ◽

SEBASTIÃO J. DA SILVA NETO

Keyword(s):

Endangered Species ◽

Atlantic Forest ◽

Endemic Species ◽

Related Species ◽

Conservation Status ◽

Type Specimen ◽

Brazilian Atlantic Forest ◽

Closely Related Species ◽

Floristic Studies ◽

Occurrence Records

ABSTRACT Taxonomic and floristic studies in the state of Rio de Janeiro allowed the rediscovery of Miconia gigantea, an endemic species to the Atlantic Forest, until recently known only from the type specimen, collected over 100 years by A.F.M. Glaziou. We present an amended and detailed description of M. gigantea, providing characteristics of the flowers, fruits and seeds, in addition to illustrations, comments about taxonomic affinities with closely related species, the presently known distribution together with new occurrence records, and the conservation status.

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Balancing effective conservation with sustainable resource use in protected areas: precluded by knowledge gaps

Environmental Conservation ◽

10.1017/s0376892914000320 ◽

2014 ◽

Vol 42 (3) ◽

pp. 246-255 ◽

Cited By ~ 7

Author(s):

NICOLA J. VAN WILGEN ◽

MELODIE A. MCGEOCH

Keyword(s):

Protected Areas ◽

National Parks ◽

Resource Use ◽

Conservation Status ◽

Marine Species ◽

Trade Offs ◽

Sustainable Resource Use ◽

Management Capacity ◽

Harvest Sustainability ◽

Adequate Data

SUMMARYDespite significant expansion of the global protected area (PA) network, this investment has not commonly been matched by investment in their management. This includes managing trade-offs between social and biodiversity goals, including resource use in PAs. While some resource-use activities receive significant attention, the full suite of resources extracted from PA systems is rarely documented. This paper illustrates the potential risk of resource use to PA ecological performance through a survey of resources harvested in South Africa's national parks. Even for this comparatively well-managed suite of parks, significant data gaps preclude assessments of harvest sustainability. Harvest quantities were known for < 8% of the 341 used resources, while 23% were not identified to species level. International Union for the Conservation of Nature Red List conservation status had not been evaluated for 78% of species, and 31% of all species (83% of marine species) had not been evaluated nationally. Protected areas face ongoing pressure to balance people-based and biodiversity outcomes, but whether or not both objectives can be achieved cannot be assessed without adequate data. Managing PAs in future will require consideration of trade-offs between investing in PA expansion, increasing the monitoring and management capacity of PA agencies, and investing in the research needed to support decision making.

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Microhabitat selection in the potentially endangered wolf spider Donacosa merlini (Araneae, Lycosidae): implications for spider conservation

Canadian Journal of Zoology ◽

10.1139/z08-113 ◽

2008 ◽

Vol 86 (11) ◽

pp. 1280-1288 ◽

Cited By ~ 6

Author(s):

C. Fernández-Montraveta ◽

M. Cuadrado

Keyword(s):

Habitat Selection ◽

Habitat Quality ◽

Spatial Scales ◽

Conservation Status ◽

Regional Scale ◽

Wolf Spider ◽

Distribution Area ◽

Suitable Habitat ◽

Ecological Requirements ◽

Trade Offs

Habitat quality affects many components of animal fitness and animals are expected to be distributed in the space accordingly. Mismatch between habitat preferences and fitness may relate to scale-dependent effects and trade-offs between costs and benefits of moving to high-quality habitats. We investigated the effects of habitat quality and habitat selection in Donacosa merlini Alderweireldt and Jocqué, 1991, a burrowing wolf spider included in the Spanish Invertebrates Red Data Book. Particularly, we compared burrow size and density and analysed the relationship between burrow presence and vegetation at two different scales. At a regional scale, we found strong differences in burrow size and density. Burrow density affected burrow aggregation, which was utmost under mean densities. At both spatial scales, burrows were found at relatively clear (or low-covered) patches, as scrubs were lower and nearest vegetation was farther from burrows than randomly expected. Our results suggest habitat selection and effects of habitat quality on the life history of D. merlini. In spite of the recent expansion of the species distribution area, our data support the need for suitable habitat management programs. Information about ecological requirements is paramount to correctly assess spider conservation status. This topic has received little attention in spite of the diversity and the relevance of spider ecological roles.

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The predictability of a lake phytoplankton community, from hours to years

10.1101/230722 ◽

2017 ◽

Author(s):

Mridul K. Thomas ◽

Simone Fontana ◽

Marta Reyes ◽

Michael Kehoe ◽

Francesco Pomati

Keyword(s):

Machine Learning ◽

Cell Density ◽

Algal Cell ◽

Biotic Interactions ◽

Ecological Communities ◽

Trade Offs ◽

Data Limitations ◽

Frequency Monitoring ◽

Phytoplankton Cell ◽

Physical And Chemical

AbstractForecasting anthropogenic changes to ecological communities is one of the central challenges in ecology. However, nonlinear dependencies, biotic interactions and data limitations have limited our ability to assess how predictable communities are. Here we used a machine learning approach and environmental monitoring data (biological, physical and chemical) to assess the predictability of phytoplankton cell density in one lake across an unprecedented range of time scales. Communities were highly predictable over hours to months: model R2 decreased from 0. 89 at 4 hours to 0.75 at 1 month, and in a long-term dataset lacking fine spatial resolution, from 0.46 at 1 month to 0.32 at 10 years. When cyanobacterial and eukaryotic algal cell density were examined separately, model-inferred environmental growth dependencies matched laboratory studies, and suggested novel trade-offs governing their competition. High-frequency monitoring and machine learning can help elucidate the mechanisms underlying ecological dynamics and set prediction targets for process-based models.

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