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Figure 3 from: Ziv G, Beckmann M, Bullock J, Cord A, Delzeit R, Domingo C, Dreßler G, Hagemann N, Masó J, Müller B, Neteler M, Sapundzhieva A, Stoev P, Stenning J, Trajković M, Václavík T (2020) BESTMAP: behavioural, Ecological and Socio-economic Tools for Modelling Agricultural Policy. Research Ideas and Outcomes 6: e52052. https://doi.org/10.3897/rio.6.e52052
Figure 3 The Land Systems Archetypes mapped by Levers et al. (2018) for one of BESTMAP case study areas in Catalonia. Land Systems Archetypes were derived at 3km resolution for Europe based on CORINE land cover, downscaled ecological and economic data. BESTMAP aims to map Farming System Archetypes (FSAs) at the farm level.
Figure 7 from: Ziv G, Beckmann M, Bullock J, Cord A, Delzeit R, Domingo C, Dreßler G, Hagemann N, Masó J, Müller B, Neteler M, Sapundzhieva A, Stoev P, Stenning J, Trajković M, Václavík T (2020) BESTMAP: behavioural, Ecological and Socio-economic Tools for Modelling Agricultural Policy. Research Ideas and Outcomes 6: e52052. https://doi.org/10.3897/rio.6.e52052
Figure 7 BESTMAP workflow to map FSAs, defined as having a characteristic bundle of ESS (and dis-services e.g. pollution), biodiversity, socio-economic and behavioural characteristics. Geospatial datasets (the Case Study Base Layer) will be used to approximate these FSAs (the "Proto-FSAs" clusters) in the first step; followed by interviews, field data collection and modelling to assign values on ESS, biodiversity, socio-economic aspects and behaviour for a stratified sample of proto-FSAs farms. The proto-FSAs will likely share "socio-economic-environmental" space and will be split/merged to minimize (but not eliminate) that overlap by changing the clustering in Case Study Base Layer space, defining the final FSAs and their spatial distribution.
Figure 9 from: Ziv G, Beckmann M, Bullock J, Cord A, Delzeit R, Domingo C, Dreßler G, Hagemann N, Masó J, Müller B, Neteler M, Sapundzhieva A, Stoev P, Stenning J, Trajković M, Václavík T (2020) BESTMAP: behavioural, Ecological and Socio-economic Tools for Modelling Agricultural Policy. Research Ideas and Outcomes 6: e52052. https://doi.org/10.3897/rio.6.e52052
Figure 9 BESTMAP dashboard mock-up, visualizing the impact of the policies through the policy indicators.
Figure 8 from: Ziv G, Beckmann M, Bullock J, Cord A, Delzeit R, Domingo C, Dreßler G, Hagemann N, Masó J, Müller B, Neteler M, Sapundzhieva A, Stoev P, Stenning J, Trajković M, Václavík T (2020) BESTMAP: behavioural, Ecological and Socio-economic Tools for Modelling Agricultural Policy. Research Ideas and Outcomes 6: e52052. https://doi.org/10.3897/rio.6.e52052
Figure 8 BESTMAP ABM concept. Individual farmer's decision-making is modelled using the RAA (orange elements). Each farmer agent has an internal state that is characterized by goals & needs (profit maximization, safety first, risk aversion, etc.), knowledge (education, farming strategies, beliefs about consequences of different practices, etc.), values (social norms, strength of beliefs, value of conservation or biodiversity, etc.) and assets (monetary resources, production means, information, etc.). These shape farmer's attitude towards different behaviours (e.g. pro or contra organic farming), perceived norm (social pressure or other farmer's behaviour) and perceived behavioural control (agent's capacity to perform certain behaviours). Every farmer agent can perceive the state of the farming system (e.g. yields, ESS provision), the behaviour of other farmers as well as exogenous drivers (e.g. price or policy changes). Evaluating these lead to changes in attitudes or perceived norm, which can expand or reduce its behavioural options. Each behavioural option represents an intention to perform a certain behaviour (e.g. adopting a different crop choice or setting aside land). Using a multi-objective utility function, the farmer agent will select a specific behavioural option that will change the state of its farm's fields and provide some economic benefit to the farmer. Specifically, farmer's decisions can lead to changes in cropping system, farm and field size, or in farm ownership, which allows us to analyse/model structural change.
Figure 4 from: Ziv G, Beckmann M, Bullock J, Cord A, Delzeit R, Domingo C, Dreßler G, Hagemann N, Masó J, Müller B, Neteler M, Sapundzhieva A, Stoev P, Stenning J, Trajković M, Václavík T (2020) BESTMAP: behavioural, Ecological and Socio-economic Tools for Modelling Agricultural Policy. Research Ideas and Outcomes 6: e52052. https://doi.org/10.3897/rio.6.e52052
Figure 4 BESTMAP conceptual framework for policy impact assessment modelling, combining actions at EU scale (green), and activities within a representative set of case studies (orange; demonstrated in five areas within BESTMAP). The framework combines workshops (ovals), modelling (rounded rectangles) and interviews (ovals) producing several datasets (curved rectangles) and an online interactive dashboard (barrel shape). At the EU level, BESTMAP will co-design with policy-makers and stakeholders policy scenarios which, with existing scenarios of climate change and other global events, will input into the global economic models (here DART-BIO CGE model, but also MAGNET, CAPRI etc.). Georeferenced data layers ("Case Study Base Layers"; CBL) will be collated from existing sources including the Land Parcel Identification System (LPIS), Farm Accountancy Data Network (FADN), CORINE land-use/land cover, INSPIRE Geoportal, Copernicus Land Monitoring, national/regional datasets and existing remote sensing products. This will be used to define 'prototype' Farm System Archetypes (FSAs) which help stratify and design the interview campaign in each of the case studies. BESTMAP will collect demographic, behavioural characteristics and socio-economic information using semi-structured interviews (with harmonized protocols). Ecosystem services models (here the InVEST biophysically based models, but simpler e.g. capacity matrix or more complex e.g. SWAT models can in be used in the future) will estimate ESS, environmental/climatic impacts and biodiversity provided by different farming units. The 'bundle' of ESS, geo-statistically modelled socio-economics and behavioural characteristics will add to the CBL and define the final typology of FSAs in the case study. A generic ABM template based on the RAA behavioural theory will be locally adapted by the same interviews/surveys, and driven by the outputs (prices and costs of commodities, energy etc.) from the global economic model (DART-BIO) as well as narratives and drivers arising from a national/regional workshop interpreting the EU-level policy. The change in FSAs resulting from those scenarios via the ABM will translate to change in ESS and socio-economics defining the FSA 'bundles' including their uncertainty. Those impacts at the case study level will be translated into stakeholders-defined policy indicators (e.g. of the SDGs) and visualized using a standard interactive web-based data portal policy dashboard, the use of which will be the focus of training and dissemination activities.
Figure 1 from: Ziv G, Beckmann M, Bullock J, Cord A, Delzeit R, Domingo C, Dreßler G, Hagemann N, Masó J, Müller B, Neteler M, Sapundzhieva A, Stoev P, Stenning J, Trajković M, Václavík T (2020) BESTMAP: behavioural, Ecological and Socio-economic Tools for Modelling Agricultural Policy. Research Ideas and Outcomes 6: e52052. https://doi.org/10.3897/rio.6.e52052
Figure 1 Top-level conceptual framework of BESTMAP. BESTMAP combining existing global/EU scale (green) models used by the EC, in particular Partial Equilibrium (PE) and Computable General Equilibrium (CGE) models with regional analyses (yellow) by formalizing engagement with stakeholders to define scenarios; using existing geospatial data and empirical data collection to map farming systems based on a novel concept of Farming System Archetypes (FSA) namely farms with a characteristic bundle of ESS, biodiversity, socio-economics and behavioural characteristics of decision-making agents (i.e. farmers); linking economic (typically global) large scale economic based PE/CGE to agent-based models; describing changes in FSAs's distribution, ESS, biodiversity and socio-economics in a representative sample of case study areas.
Figure 5 from: Ziv G, Beckmann M, Bullock J, Cord A, Delzeit R, Domingo C, Dreßler G, Hagemann N, Masó J, Müller B, Neteler M, Sapundzhieva A, Stoev P, Stenning J, Trajković M, Václavík T (2020) BESTMAP: behavioural, Ecological and Socio-economic Tools for Modelling Agricultural Policy. Research Ideas and Outcomes 6: e52052. https://doi.org/10.3897/rio.6.e52052
Figure 5 BESTMAP demonstration case studies: overview of CS locations in the context of Land Systems Archetypes mapped by Levers et al. 2018 at 3km resolution for Europe. No land system archetypes shown for Serbia as the mapping included EU countries only. Insets show the exact boundaries of CS areas.
Figure 2 from: Ziv G, Beckmann M, Bullock J, Cord A, Delzeit R, Domingo C, Dreßler G, Hagemann N, Masó J, Müller B, Neteler M, Sapundzhieva A, Stoev P, Stenning J, Trajković M, Václavík T (2020) BESTMAP: behavioural, Ecological and Socio-economic Tools for Modelling Agricultural Policy. Research Ideas and Outcomes 6: e52052. https://doi.org/10.3897/rio.6.e52052
Figure 2 The conceptualization of Farming System Archetypes (FSAs) in BESTMAP is an extension of land-use intensity framework from Erb et al. 2013 to include the land manager/farmer and its behavioural characteristics (based here on the RAA). FSA have a typical 'bundle' of ESS, outputs, outcomes, inputs and farmer characteristics.
Figure 5 from: Coelho JR, Hastings JM, Holliday CW (2020) Nesting ecology of the Pacific cicada killer, Sphecius convallis Patton (Hymenoptera, Crabronidae), in the Sonoran Desert. Journal of Hymenoptera Research 80: 177-191. https://doi.org/10.3897/jhr.80.59206
Figure 5 Sphecius convallis feeding on sap at a willow tree, along with several flies. Note the foam in the wound in the tree's bark, probably made by yeast sugar fermentation.
Figure 2 from: Coelho JR, Hastings JM, Holliday CW (2020) Nesting ecology of the Pacific cicada killer, Sphecius convallis Patton (Hymenoptera, Crabronidae), in the Sonoran Desert. Journal of Hymenoptera Research 80: 177-191. https://doi.org/10.3897/jhr.80.59206
Figure 2 Screen tent used in estimating male cicada killer population size. Field of mine tailings in the background.
Data from: A land classification protocol for pollinator ecology research: an urbanisation case study
1. Land-use change is one of the most important drivers of widespread declines in pollinator populations. Comprehensive quantitative methods for land classification are critical to understanding these effects, but co-option of existing human-focussed land classifications is often inappropriate for pollinator research. 2. Here we present a flexible GIS-based land classification protocol for pollinator research using a bottom-up approach driven by reference to pollinator ecology, with urbanisation as a case study. Our multi-step method involves manually generating land cover maps at multiple biologically relevant radii surrounding study sites using GIS, with a focus on identifying land cover types that have a specific relevance to pollinators. This is followed by a three-step refinement process using statistical tools: 1) definition of land-use categories, 2) Principal Components Analysis (PCA) on the categories and 3) cluster analysis to generate a categorical land-use variable for use in subsequent analysis. Model selection is then used to determine the appropriate spatial scale for analysis. 3. We demonstrate an application of our protocol using a case study of 38 sites across a gradient of urbanisation in South-East England. In our case study, the land classification generated a categorical land-use variable at each of four radii based on the clustering of sites with different degrees of urbanisation, open land and flower-rich habitat. 4. Studies of land-use effects on pollinators have historically employed a wide array of land classification techniques from descriptive and qualitative to complex and quantitative. We suggest that land-use studies in pollinator ecology should broadly adopt GIS-based multi-step land classification techniques to enable robust analysis and aid comparative research. Our protocol offers a customizable approach that combines specific relevance to pollinator research with the potential for application to a wide range of ecological questions, including agroecological studies of pest control.
Data from: Avoiding erroneous citations in ecological research: read before you apply
The Shannon–Wiener index is a popular nonparametric metric widely used in ecological research as a measure of species diversity. We used the Web of Science database to examine cases where papers published from 1990 to 2015 mislabelled this index. We provide detailed insights into causes potentially affecting use of the wrong name 'Weaver' instead of the correct 'Wiener'. Basic science serves as a fundamental information source for applied research, so we emphasize the effect of the type of research (applied or basic) on the incidence of the error. Biological research, especially applied studies, increasingly uses indices, even though some researchers have strongly criticized their use. Applied research papers had a higher frequency of the wrong index name than did basic research papers. The mislabeling frequency decreased in both categories over the 25-year period, although the decrease lagged in applied research. Moreover, the index use and mistake proportion differed by region and authors' countries of origin. Our study also provides insight into citation culture, and results suggest that almost 50% of authors have not actually read their cited sources. Applied research scientists in particular should be more cautious during manuscript preparation, carefully select sources from basic research, and read theoretical background articles before they apply the theories to their research. Moreover, theoretical ecologists should liaise with applied researchers and present their research for the broader scientific community. Researchers should point out known, often-repeated errors and phenomena not only in specialized books and journals but also in widely used and fundamental literature.
Data from: Tackling extremes: challenges for ecological and evolutionary research on extreme climatic events
1. Extreme climatic events (ECEs) are predicted to become more frequent as the climate changes. A rapidly increasing number of studies - though few on animals - suggest that the biological consequences of ECEs can be severe. 2. However, ecological research on the impacts of extreme climatic events (ECEs) has been limited by a lack of cohesiveness and structure. ECEs are often poorly defined and have often been confusingly equated with climatic variability, making comparison between studies difficult. Additionally, a focus on short-term studies has provided us with little information on the long-term implications of ECEs, and the descriptive and anecdotal nature of many studies has meant it is still unclear what the key research questions are. 3. Synthesizing the current state of work is essential to identify ways to make progress. We conduct a synthesis of the literature and discuss conceptual and practical challenges faced by research on ECEs. 4. We consider three steps to advance research. First, we discuss the importance of choosing an ECE definition and identify the pros and cons of 'climatological' and 'biological' definitions of ECEs. Second, we advocate research beyond short-term descriptive studies to address questions concerning the long-term implications of ECEs, focussing on selective pressures and phenotypically plastic responses and how they might differ from responses to a changing climatic mean. Finally, we encourage a greater focus on multi-event studies that help us understand the implications of changing patterns of ECEs, through the combined use of modelling, experimental and observational field studies. 5. This paper aims to open a discussion on the definitions, questions and methods currently used to study ECEs, which will lead to a more cohesive approach to future ECE research.
Data from: A call for more transparent reporting of error rates: the quality of AFLP data in ecological and evolutionary research
Despite much discussion of the importance of quantifying and reporting genotyping error in molecular studies, it is still not standard practice in the literature. This is particularly a concern for amplified fragment length polymorphism (AFLP) studies, where differences in laboratory, peak-calling and locus-selection protocols can generate data sets varying widely in genotyping error rate, the number of loci used and potentially estimates of genetic diversity or differentiation. In our experience, papers rarely provide adequate information on AFLP reproducibility, making meaningful comparisons among studies difficult. To quantify the extent of this problem, we reviewed the current molecular ecology literature (470 recent AFLP articles) to determine the proportion of studies that report an error rate and follow established guidelines for assessing error. Fifty-four per cent of recent articles do not report any assessment of data set reproducibility. Of those studies that do claim to have assessed reproducibility, the majority (~90%) either do not report a specific error rate or do not provide sufficient details to allow the reader to judge whether error was assessed correctly. Even of the papers that do report an error rate and provide details, many (≥23%) do not follow recommended standards for quantifying error. These issues also exist for other marker types such as microsatellites, and next-generation sequencing techniques, particularly those which use restriction enzymes for fragment generation. Therefore, we urge all researchers conducting genotyping studies to estimate and more transparently report genotyping error using existing guidelines and encourage journals to enforce stricter standards for the publication of genotyping studies.
Supplementary material 1 from: Cerrato C, Rocchia E, Brunetti M, Bionda R, Bassano B, Provenzale A, Bonelli S, Viterbi R (2019) Butterfly distribution along altitudinal gradients: temporal changes over a short time period. In: Mazzocchi MG, Capotondi L, Freppaz M, Lugliè A, Campanaro A (Eds) Italian Long-Term Ecological Research for understanding ecosystem diversity and functioning. Case studies from aquatic, terrestrial and transitional domains. Nature Conservation 34: 91-118. https://doi.org/10.3897/natureconservation.34.30728
Supplementary data
Demography, education, and research trends in the interdisciplinary field of disease ecology
<p><strong><em>Description of Supporting Files </em></strong></p> <p><strong>Demography, education, and research trends in the interdisciplinary field of disease ecology </strong></p> <p>Ellen E. Brandell, Daniel J. Becker, Laura Sampson, Kristian M. Forbes </p> <p> </p> <p><strong>TopArticles_Inclusion.xlsx</strong></p> <p>This Excel provides a list of influential articles written in by survey participants at least two times. </p> <p>Sheet “table”: just tabular information</p> <p>Sheet “withNotes”: includes notes about data, number of citations from survey participants, and percent inclusion calculations.</p> <p>Columns are:</p> <ul> <li>‘INCLUDED’: if the article appeared in the corpus (1) or not (0)</li> <li>‘COUNT’: the number of times survey participants wrote in the article</li> <li>‘ARTICLE’: article citation Percent of articles included in the corpus are calculated for 4 or more write-ins, 3-write-ins, 2 write-ins, and across all articles written in twice. </li> </ul> <p> </p> <p><strong>IRB_Correspondence_STUDY00010582.pdf</strong></p> <p>Institutional Review Board correspondence and approval from Pennsylvania State University. Survey response data may be available upon request from the corresponding author. To protect participants, any potentially identifying information will be removed prior to filling a request. See the online Supporting Information for this article for extensive reporting of survey results prior to a request. </p> <p> </p> <p><strong>FullSurvey.pdf</strong></p> <p>A PDF of the full survey form. </p> <p> </p> <p><strong>CorpusFrequencyAnalysis.ipynb</strong></p> <p>This is the Python script used for corpus organization and the topic detection analysis. It includes some plot generation.</p>
Figure 4 from: Kissling WD, Seijmonsbergen AC, Foppen RPB, Bouten W (2017) eEcoLiDAR, eScience infrastructure for ecological applications of LiDAR point clouds: reconstructing the 3D ecosystem structure for animals at regional to continental scales. Research Ideas and Outcomes 3: e14939. https://doi.org/10.3897/rio.3.e14939
Figure 4 - Time table for the eEcoLiDAR project (assuming a start in March 2017). The work plan covers tasks for the NLeSC engineers, the proposed PhD student, and two associated Postdoc projects.
Figure 3 from: Kissling WD, Seijmonsbergen AC, Foppen RPB, Bouten W (2017) eEcoLiDAR, eScience infrastructure for ecological applications of LiDAR point clouds: reconstructing the 3D ecosystem structure for animals at regional to continental scales. Research Ideas and Outcomes 3: e14939. https://doi.org/10.3897/rio.3.e14939
Figure 3 - Example of identifying trees in a forest from LiDAR data. Illustrated is a small plot of poplar trees in Flevoland, The Netherlands, for which tree crowns and tree tops have been calculated.
Figure 2 from: Kissling WD, Seijmonsbergen AC, Foppen RPB, Bouten W (2017) eEcoLiDAR, eScience infrastructure for ecological applications of LiDAR point clouds: reconstructing the 3D ecosystem structure for animals at regional to continental scales. Research Ideas and Outcomes 3: e14939. https://doi.org/10.3897/rio.3.e14939
Figure 2 - Generic workflow for object-based image analysis (OBIA) of LiDAR point clouds and proposed ecological applications. A workbench (blue) will be developed to handle the data storage, data exploration, and interactive OBIA of the massive LiDAR point clouds. Combined with datasets of bird distributions, climate, and other remote sensing layers (orange), the LiDAR data will be applied to several ecological case studies, e.g. by using species distribution modelling of birds and insect pollinators (green).
Figure 1 from: Kissling WD, Seijmonsbergen AC, Foppen RPB, Bouten W (2017) eEcoLiDAR, eScience infrastructure for ecological applications of LiDAR point clouds: reconstructing the 3D ecosystem structure for animals at regional to continental scales. Research Ideas and Outcomes 3: e14939. https://doi.org/10.3897/rio.3.e14939
Figure 1 - The vertical and horizontal distribution of plants influences habitat structure and 3D characteristics of vegetation for animals. Illustrated are examples for (a) forests, (b) agricultural and open landscapes, and (c) reedbeds and marshlands. The height, openness and density of vegetation as well as specific habitat features (e.g. tree species, hedges etc.) are key aspects of animal habitat and space use.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.