Data and information management for the monitoring of biodiversity in Alberta

Sólymos, Péter; Morrison, Shawn F.; Kariyeva, Jahan; Schieck, Jim; Haughland, Diane L.; Azeria, Ermias T.; Cobb, Tyler P.; Hinchliffe, Robert; Kittson, Jillian; McIntosh, Anne C.S.; Narwani, Tara; Pierossi, Paola; Roy, Marie‐Josée; Sandybayev, Turar; Boutin, Stan; Bayne, Erin M.

doi:10.1002/wsb.564

Cited by 8 publications

(4 citation statements)

References 11 publications

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“…The Alberta Biodiversity Monitoring Institute (ABMI) tracks biodiversity distributions and trends throughout Alberta (Sólymos et al ., ), focusing on several taxonomic groups including aquatic macroinvertebrates (http://www.abmi.ca). The field programme centres on a systematically spaced (20‐km) point grid (Fig.…”

Section: Methodsmentioning

confidence: 99%

“…Long‐term, large‐scale monitoring has become integral to biodiversity conservation in the Anthropocene (Lindenmayer & Likens, ; Burton et al ., ; Schmeller et al ., ). Major initiatives and programmes (eg, Canadian Aquatic Biomonitoring Network, Long‐Term Ecological Research Program, South African National Biodiversity Assessment, UK Breeding Bird Survey) vary in objectives and scope but share the mission to assess the distributions and trends of many species or taxonomic groups over large areas (eg, regions) through an extended period of time (Yoccoz et al ., ; Nichols & Williams, ; Nielsen et al ., ; Haughland et al ., ; Sólymos et al ., ).…”

Section: Introductionmentioning

confidence: 97%

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Improving taxonomic resolution in large‐scale freshwater biodiversity monitoring: an example using wetlands and Odonata

Bried

Hinchliffe

2018

Insect Conserv Diversity

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Immature aquatic insects are a major source of taxonomic difficulty in large‐scale freshwater biodiversity monitoring. Adult stages could improve taxonomic resolution for assessing distributions and trends of biodiversity. Odonata (dragonflies and damselflies) have accessible adult stages that should greatly enhance the amount of species‐level information. We used Odonata and a wetland monitoring programme in Alberta, Canada to illustrate how much taxonomic information can be lost in larval collections, and an extensive adult records database to estimate what could be gained from adult surveys. Despite processing 22 638 odonate specimens from 975 wetlands throughout Alberta, larval monitoring failed to collect or identify almost 60% of the lentic‐breeding Odonata species known from adult records. A total of 25 lentic‐breeding dragonfly species and 12 lentic‐breeding damselfly species were present in adult records and not the larval data, including species of conservation concern. Due to the abundance of early instars, a substantial 82% of the processed damselfly collection and 62% of the processed dragonfly collection was left at suborder. We recommend supplementing aquatic sampling with adult rearing, collecting, and observing (at least Odonata) to improve the basic inventory and overall status assessment in large‐scale freshwater biodiversity monitoring. This is especially true when aquatic sampling is restricted to a suboptimal time of year for species identifications.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%

Improving taxonomic resolution in large‐scale freshwater biodiversity monitoring: an example using wetlands and Odonata

Bried

Hinchliffe

2018

Insect Conserv Diversity

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“…that discuss the challenges of running long-term and large-scale research programs (Applegate 2015, Hunt et al 2015, Sólymos et al 2015, and Sutter et al 2015. However, the conceptual nature and comprehensive scope of these articles precludes them as a simple introduction to the topic.…”

mentioning

confidence: 99%

Systematic data management

Nordell¹,

Franke²

2017

Applied Raptor Ecology: Essentials From Gyrfalcon Research

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Systematic data management has become essential for the collection, storage, and use of "big data" common to banking, healthcare, and insurance industries. However, projects of all sizes can benefit from structured data management. The collection and storage of information using appropriate organizational structure, data entry, and metadata promotes error free and efficient manipulation, reporting, analysis, sharing, and congruity over time (Léonard 1992). In this chapter we discuss from the perspective of raptor biology how to organize information so that it can be accessed, managed, and updated easily and without error (Ritchie 2002). Recent literature has highlighted the significant benefits accrued by wildlife research programs using systematic data management (Rüegg et al. 2014, Applegate 2015, Reynolds et al. 2016). Despite the practical benefits, systematic data management remains underused by wildlife researchers (Applegate 2015). One barrier to the implementation of systematic data management is that there are few resources designed specifically to assist wildlife biologists, and virtually none for raptor researchers. Researchers developing a data management system can use Chamanara and König-Ries (2014), Briney (2015), and Reynolds et al. (2016) for examples of broad conceptual ideas for building and managing data.

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“…The second case study discusses data management challenges for a large‐scale biodiversity monitoring program in Alberta, Canada (Sólymos et al ), using strategies that are not uncommon to those of many monitoring programs. The third case study—data management for the Boreal Avian Modeling project (Barker et al )—presents an example of successful reuse of data collected by others to obtain insights not available from the original data.…”

mentioning

confidence: 99%

The importance of data management in wildlife conservation

Applegate

2015

Wildl. Soc. Bull.

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I have been hearing about the looming mandate to archive scientific data for some time. The federal government is moving to mandate this activity and you may have run into journals that require that the data be available in an archive before the article will be published. I whole-heartedly endorse making data available, but what is often lost is any guidance on how to do this! I was approached in 2014 about the Bulletin's interest in an article about wildlife data management, with a focus on providing practical advice on how to carry it out. The proposed single paper expanded to include case studies, and soon I had a Special Section on my hands. Fortunately for me, Associate Editor Roger Applegate has an interest in data management and he stepped up to shepherd this Special Section through to completion. He has done a terrific job, and I am turning the editorial over to Roger to introduce you to the Special Section on Data Management.Modern wildlife conservation is often adaptive and administered at multiple scales within partnerships. Partners include state and federal agencies and nongovernmental organizations. Partnership types include Joint Ventures (JV), Landscape Conservation Cooperatives (LCC), and a variety of species-oriented restoration partnerships such as the National Bobwhite Conservation Initiative (NBCI), Lesser Prairie-chicken Initiative, America's Longleaf Restoration Initiative, and many others.Adaptive management programs create and share data needed to track conservation accomplishments, measure success, and provide a basis for making management decisions (Williams and Brown 2012). Accomplishments may include data on area of habitat protected, managed, or purchased. Successes may include trend in species density, change in harvest levels, or presence-absence of species occurrence. In addition, research projects, modeling, and complex data analyses use and produce data of importance to management programs. All of these products and data have value in making decisions or helping to better understand natural systems.Agencies regularly measure harvest levels and species populations using a variety of methods. Land managing agencies produce data on a variety of management practices implemented on their lands. For example, agencies apply a variety of management techniques to their lands and produce statistics on these practices. Data entered and stored in the Tennessee Wildlife Resources Agency land management database, for example, are shared with 4 JVs, 2 LCCs, and LITERATURE CITED

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Data and information management for the monitoring of biodiversity in Alberta

Cited by 8 publications

References 11 publications

Improving taxonomic resolution in large‐scale freshwater biodiversity monitoring: an example using wetlands and Odonata

Improving taxonomic resolution in large‐scale freshwater biodiversity monitoring: an example using wetlands and Odonata

Systematic data management

The importance of data management in wildlife conservation

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