Effective Biodiversity Monitoring Needs a Culture of Integration

Kühl, Hjalmar S.; Bowler, Diana E.; Bösch, Lukas; Bruelheide, Helge; Dauber, Jens; Eichenberg, David; Eisenhauer, Nico; Fernández, Néstor; Guerra, Carlos A.; Henle, Klaus; Herbinger, Ilka; Isaac, Nick J. B.; Jansen, Florian; König‐Ries, Birgitta; Kühn, Ingolf; Nilsen, Erlend B.; Pe’er, Guy; Richter, Anett; Schulte, Ralf; Settele, Josef; Dam, Nicole M. van; Voigt, Maria; Wägele, Wolfgang; Wirth, Christian; Bonn, Aletta

doi:10.1016/j.oneear.2020.09.010

Cited by 92 publications

(71 citation statements)

References 90 publications

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“…It can be applied to other large‐scale research using heterogeneous occurrence data, given it can be harmonized to a common fine‐scale grid. This makes our approach valuable for other projects, such as the growing Living Atlas community (Brenton et al, 2018) and may also help to inform and create new, more collaborative monitoring schemes that integrate knowledge and data from different actors in nature protection (e.g., governmental, academical, and volunteer; Kühl et al, 2020). Such new schemes should also include long‐term monitoring of common species (see also Pescott et al, 2015).…”

Section: Resultsmentioning

confidence: 99%

Widespread decline in Central European plant diversity across six decades

Eichenberg

Bowler

Bonn

et al. 2020

Global Change Biology

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Based on plant occurrence data covering all parts of Germany, we investigated changes in the distribution of 2136 plant species between 1960 and 2017. We analyzed 29 million occurrence records over an area of ~350,000 km2 on a 5 × 5 km grid using temporal and spatiotemporal models and accounting for sampling bias. Since the 1960s, more than 70% of investigated plant species showed declines in nationwide occurrence. Archaeophytes (species introduced before 1492) most strongly declined but also native plant species experienced severe declines. In contrast, neophytes (species introduced after 1492) increased in their nationwide occurrence but not homogeneously throughout the country. Our analysis suggests that the strongest declines in native species already happened in the 1960s–1980s, a time frame in which often few data exist. Increases in neophytic species were strongest in the 1990s and 2010s. Overall, the increase in neophytes did not compensate for the loss of other species, resulting in a decrease in mean grid cell species richness of −1.9% per decade. The decline in plant biodiversity is a widespread phenomenon occurring in different habitats and geographic regions. It is likely that this decline has major repercussions on ecosystem functioning and overall biodiversity, potentially with cascading effects across trophic levels. The approach used in this study is transferable to other large‐scale trend analyses using heterogeneous occurrence data.

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Section: Resultsmentioning

confidence: 99%

Widespread decline in Central European plant diversity across six decades

Eichenberg

Bowler

Bonn

et al. 2020

Global Change Biology

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“…In the achievement of new sources of knowledge, researchers and experts adopt strategies to increase public understanding and awareness and to make the whole scientific process more participative [25] thanks to the support of web-based technologies and the combination of scientific research and social inclusion purposes [25]. Research monitoring programs are usually approached from a top-down data-centric perspective [42]. Engagement of people with a different level of expertise and knowledge can contribute to enrich point of views in research activities and enlarge research perspectives [42] by paving the way to citizen science approaches that also consider socio-cultural perspectives [42].…”

Section: Differences Between Citizen Science and Participatory Approachesmentioning

confidence: 99%

“…Research monitoring programs are usually approached from a top-down data-centric perspective [42]. Engagement of people with a different level of expertise and knowledge can contribute to enrich point of views in research activities and enlarge research perspectives [42] by paving the way to citizen science approaches that also consider socio-cultural perspectives [42].…”

Section: Differences Between Citizen Science and Participatory Approachesmentioning

confidence: 99%

“…A community is a complex group that can involve different kinds of actors such as, (1) expert scientists in the role of coordinators, (2) decision makers (3) stakeholders and (4) the general public or citizens [5,42] that share a common vision of society and organization [42]. However, different typologies of participants within the community can imply a different level or stage of involvement within the participatory process according to expertise, experience, and personal interests [5].…”

Section: Recruitment and Motivationsmentioning

confidence: 99%

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Towards a Transdisciplinary Theoretical Framework of Citizen Science: Results from a Meta-Review Analysis

et al. 2021

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This work intends to lay the foundations for a theoretical framework of citizen science combining social and organizational implications with the support of information technologies. The proposed theoretical framework moves towards a shared and common research process between experts and citizens to deal with environmental and social challenges. The role and capacity of online communities is explored and their engagement capacity by means of web-based digital platforms supporting crowdsourcing activities. In this contribution, authors highlight the most common practices, methods and issues of citizen science approaches adopted from multidisciplinary application fields to obtain insights for designing a new participative approach for organizational studies. To reach this goal, authors illustrate the results of a systematic meta-review analysis, consisting of an accurate selection and revision of journal review articles in order to highlight concepts, methods, research design approaches and tools adopted in citizen science approaches.

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“…Estimating and conserving biodiversity requires studies of extensive areas that are independent from administrative boundaries [10]. However, insufficient data, including the lack of species surveys and geographical data gaps, are obstacles to understanding and measuring species biodiversity [7,11,12].…”

Section: Introductionmentioning

confidence: 99%

Mapping Potential Plant Species Richness over Large Areas with Deep Learning, MODIS, and Species Distribution Models

2021

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The spatial patterns of species richness can be used as indicators for conservation and restoration, but data problems, including the lack of species surveys and geographical data gaps, are obstacles to mapping species richness across large areas. Lack of species data can be overcome with remote sensing because it covers extended geographic areas and generates recurring data. We developed a Deep Learning (DL) framework using Moderate Resolution Imaging Spectroradiometer (MODIS) products and modeled potential species richness by stacking species distribution models (S-SDMs) to ask, “What are the spatial patterns of potential plant species richness across the Korean Peninsula, including inaccessible North Korea, where survey data are limited?” First, we estimated plant species richness in South Korea by combining the probability-based SDM results of 1574 species and used independent plant surveys to validate our potential species richness maps. Next, DL-based species richness models were fitted to the species richness results in South Korea, and a time-series of the normalized difference vegetation index (NDVI) and leaf area index (LAI) from MODIS. The individually developed models from South Korea were statistically tested using datasets that were not used in model training and obtained high accuracy outcomes (0.98, Pearson correlation). Finally, the proposed models were combined to estimate the richness patterns across the Korean Peninsula at a higher spatial resolution than the species survey data. From the statistical feature importance tests overall, growing season NDVI-related features were more important than LAI features for quantifying biodiversity from remote sensing time-series data.

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Effective Biodiversity Monitoring Needs a Culture of Integration

Cited by 92 publications

References 90 publications

Widespread decline in Central European plant diversity across six decades

Widespread decline in Central European plant diversity across six decades

Towards a Transdisciplinary Theoretical Framework of Citizen Science: Results from a Meta-Review Analysis

Mapping Potential Plant Species Richness over Large Areas with Deep Learning, MODIS, and Species Distribution Models

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