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Figure 16 in Morphometrics: History, development methods and prospects
Figure 16. Gross dorsal fin shape differences between two species included in the example fish dataset. See text for discussion.
Figure 13 in Morphometrics: History, development methods and prospects
Figure 13. Cross sections through the Procrustes shape space illustrating various spatial relations between the reference (red) and target (green) configurations. All Procrustes-aligned landmark configurations exists as point locations on the surfaces of hyperdimensional manifolds the radius of which is determined by the configurations' size. Left. Between any two configurations the Procrustes Distance (D) is the length of the shortest arc between the configurations points on the shape manifold's surface. The Partial Procrustes Distance (Dp) is the linear distance between these points. For point comparisons for which the arc angle (Ɵ) is small Dp will provide a good estimate of D. Interestingly, the closer match between the target and reference configurations points can be found by relaxing the constraint of size similarity. The Full Procrustes Distance (Df) is the minimal distance between the reference and the target configurations when the size of the target is allowed to vary, but not its shape. Right. The distribution of a shape or series of shapes with respect to a reference shape may be portrayed in the linear spaces that have become traditional in pre-geometric morphometrics by orthogonally projecting their positions on the Procrustes shape manifold to a linear plane. This operation can be accomplished via covariance-based PCA of the complete set of partial warp scores (= relative warps analysis) or direct covariancebased PCA of the Procrustes shape coordinates expressed as deviations from the reference shape. Under this convention the projection plane will be tangent to the shape manifold at the point of the reference shape and exhibit a reduced dimensionality from that of the shape manifold's surface. If the total range of shape variation present within the sample is small the apparent Procrustes distances (Dp) between point locations in this tangent space will be good estimates of both the Partial Procrustes Distance (Dp) and (by extension) the Procrustes Distance (D). Redrawn from an original diagram by F. J. Rohlf.
Figure 11 in Morphometrics: History, development methods and prospects
Figure 11. Thin plate spline representations of the isotropic (= uniform) and anisotropic (= non-uniform) components of shape variation implicit in the comparison between D'Arcy Thompson's (1917) drawings of Argyroplecus and Sternoptyx after GLS Procrustes alignment the landmark configurations. For this comparison the non-uniform component was calculated using the complement procedure described by Rohlf and Bookstein (2003). By convention there is no bending energy associated with the uniform component of shape deformation. D'Arcy Thompson identified the dominant uniform dorsal-ventral shear component of this shape transition correctly, but did not identify (or discuss) the non-uniform component, which he may have regarded as unstructured or random variation.
Figure 15 in Morphometrics: History, development methods and prospects
Figure 15. Major structure of shape variation for the fish sample as assessed the boundary outline semilandmark data (Fig. 5C). See text for discussion.
Figure 8 in Morphometrics: History, development methods and prospects
Figure 8. Principal components ordinations of the Procrustes-aligned triangle configurations shown in Figure 6.
Figure 8 in Morphometrics: History, development methods and prospects
Figure 8 shows results of a covariance-based PCA of these Procrustes aligned shape-coordinate data both in terms of the first two (left) and a complete set of three (right) principal components.1 Each point in this space represents a unique configuration of the six coordinate values that, together, comprise any triangle. But unlike the width/height space (or a PCAdetermined representation thereof, see Fig. 6), all but three triangles in each group exhibit unique coordinate locations in the
Figure 19 in Morphometrics: History, development methods and prospects
Figure 19. Results of an adaptive semilandmark interpolation analysis in which the Argyroplecus and Sternoptyx outlines, originally sampled with 250 semilandmark points, were interpolated using 13 landmark points located on the outline (blue = boundary outline termination landmarks, red = landmarks). Use of these landmark points breaks the outline into 12 segments which can then be interpolated separately using n equally spaced semilandmark points (green) where n equals the number of semilandmark points required to ensure a geometric fidelity fit of 95% of the length of the original (digitized) curve. This procedure ensures all aspects of the boundary outline are matched exactly at, and in the vicinity of the landmark points and ensures that all segments of the outline are represented to a consistent geometric representational standard. In order to meet these criteria over the entire outline for both forms 83 points were necessary. The eye, gill opercula dorsal terminus and the pectoral fin were excluded from this analysis, but could easily be added using identical criteria to constrain their representation, as could additional landmark points.
Figure 14 in Morphometrics: History, development methods and prospects
Figure 14. Major structure of shape variation for the fish sample as assessed the landmark data (Fig. 5B). See text for discussion.
Figure 7 in Morphometrics: History, development methods and prospects
Figure 7 shows the result of a Procrustes alignment of vertex coordinates for the groups of triangles shown in Figure 6. This alignment expresses the least squares deviation of each triangle from the mean triangle which, for this dataset, happens
Figure 12 in Morphometrics: History, development methods and prospects
Figure 12. Thin plate spline interpolations of selected principle warps for Argyroplecus illustrating the increasing spatial generality, and decreasing bending energy, associated with these orthogonal components of non-uniform deformation. See text for discussion.
Figure 5 in Morphometrics: History, development methods and prospects
Figure 5. Alternative datasets that can represent the geometric aspects of an object or specimen's form. Landmarks shown in red. Boundary-outline curve termination landmarks shown in blue. Boundary outline semilandmarks shown in green. Note differences in the completeness, degree of abstraction, focus of representation, and sizes of these alternatives. Original image is a greyscale rendering of the porkfish (Anisotremus virginicus) published originally as a color figure in Shaw and Nodder (1799).
Figure 4. A in Morphometrics: History, development methods and prospects
Figure 4. A selection of standard linear distance measurements used to quantify form variation in fish. Note the level of redundancy and uneven coverage of the form inherent in these measurements as well as the fact that the endpoints (= landmarks) that define these distances are not always located at topologically corresponding locations on biologically homologous structures. Fish image courtesy of The Natural History Museum (London, image no. BMNH 413511).
Figure 18 in Morphometrics: History, development methods and prospects
Figure 18. Equally-spaced boundary outline semilandmark coordinate representations of Thompson's Argyoplecus and Sternoptyx images using 50-point and 150-point sampling schemes. White icons represent every 10th semilandmark in the 50-point outline sequence and every 25th semilandmark in the 150-point outline sequence. Note how the detail of the outlines present in the original drawings has been smoothed in both schemes. Nevertheless, the representational quality is much greater in both schemes than for the landmark sample scheme shown in Fig. 5B. Note also that the level of mismatch between comparable points in both semilandmark sequences, with respect to biologically homologous structures, is remarkably good, though there is an obvious mismatch of points between these two outlines in the dorsal fin of the 150-point sampling scheme.
Figure 1 in Morphometrics: History, development methods and prospects
Figure 1. Drawing of a staircase using simple two-point perspective with the construction lines left in. Renaissance artists such as Lorenzetti, Brunelleschi and Alberti used graphical methods such as these to represent the forms of three-dimensional objects on twodimensional surfaces — an early type of graphic morphometric analysis.
Figure 3. D in Morphometrics: History, development methods and prospects
Figure 3. D'Arcy Thompson's (1917) deformation grid analysis of morphological variation for the transformation between Argyroplecus and Sternoptyx. Although these diagrams appeared intriguingly mathematical in design Thompson created them using qualitative techniques only. Redrawn from Thompson (1917).
Figure 2 in Morphometrics: History, development methods and prospects
Figure 2. Examples of graphical morphometric analysis. Left. Leonardo daVinci's Virtuvian Man which is a graphical analysis of ideal or average human proportions (c. 1480). Center. Albrecht's Düer's sketch of ideal human arm proportions relative to other regional of the body (c. 1524). Right. Albrecht's Düer's sketches of human facial proportions as a system of isotropic and anisotropic deformations, from Vire Bücher von Menchlicher Proportion (1524).
Figure 9 in Morphometrics: History, development methods and prospects
Figure 9. Left. Representation of the surface of the Kendall shape space for triangles with the pole position occupied by an equilateral triangle. Each point on the surface of the sphere represents a unique configuration of 3 2D coordinate points as illustrated by the triangle diagrams. Note isosceles triangles occupy a meridian, right triangles that verge left or right occupy two meridians, and colinear triangles occupy the equator. Triangles in the lower hemisphere of this shape space are reflections across the baseline of triangles in the upper hemisphere. These two hemispheres collapse under Procrustes alignment as the lower hemisphere triangles are rotated to match the shapes of the upper hemisphere triangles. Right. Principal components ordination of 500 Procrustes-aligned random triangle configurations.
Data for 'A multi‐methods approach for assessing how conserving biodiversity interacts with other sustainable development goals in Nepal'
<p>Title: Data for 'A multi-methods approach for assessing how conserving biodiversity interacts with other sustainable development goals in Nepal'</p> <p>Recommended citation: Adhikari, B., Urbach, D., Chettri, N., Sharma, E., Breu, T., Geschke, J., Fischer, M. & Prescott, G. W. (2023) A multi-methods approach for assessing how conserving biodiversity interacts with other sustainable development goals in Nepal. Sustainable Development https://doi.org/10.1002/sd.2582</p> <p>R code available at: https://github.com/biraj-ad/SDGIntearctions_Nepal_2023</p> <p>Principal Investigator: Graham W Prescott (graham.prescott.research@gmail.com)</p> <p>Authors:<br> Biraj Adhikari (biraj.adhikari@unibe.ch, ORCID: 0000-0002-4260-8706)<br> Davnah Urbach (davnah.payne@unibe.ch, ORCID: 0000-0001-9170-7834)<br> Nakul Chettri (nakul.chettri@icimod.org, ORCID: 0000-0002-3338-8879)<br> Eklabya Sharma (eklabya.sharma11@gmail.com, ORCID:0000-0003-3089-8838)<br> Thomas Breu (thomas.breu@unibe.ch, ORCID: 0000-0003-2348-504X)<br> Jonas Geschke (jonas.geschke@unibe.ch, ORCID: 0000-0002-5654-9313)<br> Markus Fischer (markus.fischer@ips.unibe.ch, ORCID: 0000-0002-5589-5900)<br> Graham W Prescott (graham.prescott.research@gmail.com, ORCID:0000-0001-5123-514X)</p> <p>Date of data collection: August 2021 - June 2022<br> Location of data collection: Kathmandu<br> Date of final release:</p> <p>Data Overview:<br> There are two excel files. Each excel sheet is also uploaded as a separate .csv file.</p> <p>1. 'Alldata.xls': This excel file contains data collected for three independent methods used to develop the study. There are four sheets in this excel workbook. The first three sheet pertains to data collected for the seven-point SDG interactions score, correlation, and expert elicitation method. The last sheet is a derivative of the first three methods, which contains synthesized information of data.<br> Sheet overview:<br> method1: This sheet relates to the data collected through online expert survey using Kobo Toolbox. Description of columns:<br> institution -> institutional affiliation of the participant<br> inst_cat -> categorization of the institution into Intergovernmental Organization (IGO), Non-governmental Organization (NGO), Academia, and Government.<br> exp_years -> experience (in years) of the participant in the conservation sector of Nepal<br> sdgout and sdgin -> The SDG that the participant was randomly assigned to rate its outgoing and incoming interaction with SDG 15 respectively<br> outscore -> the outgoing interaction score assigned by the participant, consisting of values between -3 (Cancelling) to +3 (Indivisible)<br> conf_out -> the degree of confidence of the participant in their answer to the outgoing interaction score<br> text_out -> (optional) a description of why the participant gave that outgoing interaction score<br> inscore -> the incoming interaction score assigned by the participant, consisting of values between -3 (Cancelling) to +3 (Indivisible)<br> conf_in -> the degree of confidence of the participant in their answer to the incoming interaction score<br> text_in -> (optional) a description of why the participant gave that incoming interaction score</p> <p>method2: This sheet relates to the data collected for correlation analysis. This includes time-series data of SDG indicators for Nepal obtained from the Global SDG Indicators Database (https://unstats.un.org/sdgs/indicators/database/). Description of columns:<br> Year -> Year when the indicator was measured<br> forest_cover -> Forest cover as a percent of total area (%)<br> kba_freshwater -> Average proportion of Freshwater Key Biodiversity Areas (KBAs) covered by protected areas (%)<br> kba_terrestiral -> Average proportion of Terrestrial Key Biodiversity Areas (KBAs) covered by protected areas (%)<br> redlist -> Red List Index<br> undernourishment -> Prevalence of undernourishment (%)<br> food_insecurity -> Prevalence of moderate or severe food insecurity in the adult population (%)<br> death_chronic -> Mortality rate attributed to cardiovascular disease, cancer, diabetes or chronic respiratory disease (probability)<br> suicide -> Suicide mortality rate (deaths per 100,000 population)<br> uhc_index -> Universal health coverage (UHC) service coverage index<br> f_parliament -> Proportion of seats held by women in national parliaments (% of total number of seats)<br> safewater -> Proportion of population using safely managed drinking water services (%)<br> electricity -> Proportion of population with access to electricity, by urban/rural (%)<br> cleanfuel -> Proportion of population with primary reliance on clean fuels and technology (%)<br> renewable -> Renewable energy share in the total final energy consumption (%)<br> gdp_capita -> Annual growth rate of real GDP per capita (%)<br> manu_value -> Manufacturing value added as a proportion of GDP (%)<br> consumption_gdp -> Domestic material consumption per unit of GDP (kilograms per constant 2010 United States dollars)<br> consumption_all -> Domestic material consumption (tonnes)<br> consumption_cap -> Domestic material consumption per capita (tonnes)<br> death_missing -> Number of deaths, missing persons and persons affected by disaster per 100,000 people<br> local_drr -> Proportion of local governments that adopt and implement local disaster risk reduction strategies in line with national disaster risk reduction strategies (%)</p> <p>method3: This sheet relates to the data collected through key informant interviews. We obtained the count of interactions by coding interview responses as outgoing or incoming co-benefits or trade-offs in MaxQDA. The sheet summarizes the count for type of interaction (co-benefit or trade-off) for each SDG.</p> <p>allmethods: This sheet summarizes the proportion of co-benefits or trade-offs uncovered by each method for each SDG.</p> <p>2. 'ForSynthesistable.xlsx': This excel file synthesizes goal level interactions for the three methods in three separate sheets. We use this table to develop figure 6 of the Manuscript.</p> <p> </p>
Supplementary Information for "Resolution and the Detection of Cultural Dispersals: development and application of spatiotemporal methods in Lowland South America"
<p>Data and code to reproduce the analyses in "Resolution and the Detection of Cultural Dispersals: development and application of spatiotemporal methods in Lowland South America"</p> <p> </p> <p><strong>Abstract</strong></p> <p>Inferring episodes of expansion, admixture, diffusion, and/or migration in prehistory is at present undergoing a resurgence in macro-scale archaeological interpretation. In parallel to this renewed popularity, expanding access to computational tools and datasets has seen the use of aggregated radiocarbon datasets for the study of dispersals also increasing. This paper advocates for developing reflexive practice in the application of radiocarbon dates to prehistoric dispersals, by reflecting on the quality and qualities of the underlying data, particularly chronometric uncertainty, and framing dispersals explicitly in terms of hypothesis testing. This paper draws on cultural expansions within South America and employs two emblematic examples, the Arauquinoid and Tupiguarani traditions, to develop an analytical solution that not only incorporates chronometric uncertainty in bivariate regression but, importantly, tests whether the datasets provide statistically significant evidence for a dispersal process. The analysis, which the paper provides the means to replicate, identifies fundamental issues with resolution and data quality that impede identification of pre-Columbian cultural dispersals through simple spatial gradients of radiocarbon data. The results suggest that reflexivity must be fed back into theoretical frameworks of prehistoric mobility for the study of dispersals, in turn informing the construction of more critical statistical null models. As a first step, alternative models of cultural expansion should be formally considered alongside demographic models.Inferring episodes of expansion, admixture, diffusion, and/or migration in prehistory is at present undergoing a resurgence in macro-scale archaeological interpretation. In parallel to this renewed popularity, expanding access to computational tools and datasets has seen the use of aggregated radiocarbon datasets for the study of dispersals also increasing. This paper advocates for developing reflexive practice in the application of radiocarbon dates to prehistoric dispersals, by reflecting on the quality and qualities of the underlying data, particularly chronometric uncertainty, and framing dispersals explicitly in terms of hypothesis testing. This paper draws on cultural expansions within South America and employs two emblematic examples, the Arauquinoid and Tupiguarani traditions, to develop an analytical solution that not only incorporates chronometric uncertainty in bivariate regression but, importantly, tests whether the datasets provide statistically significant evidence for a dispersal process. The analysis, which the paper provides the means to replicate, identifies fundamental issues with resolution and data quality that impede identification of pre-Columbian cultural dispersals through simple spatial gradients of radiocarbon data. The results suggest that reflexivity must be fed back into theoretical frameworks of prehistoric mobility for the study of dispersals, in turn informing the construction of more critical statistical null models. As a first step, alternative models of cultural expansion should be formally considered alongside demographic models.</p>
Data for: Information accessibility, accounting manipulation, and sustainable development of digital enterprises: Based on double moderating effect model and panel PSM-DID method
<p>A theoretical mechanism was analyzed from the micro perspective of the enterprise to explore how information accessibility moderates the effect of accounting manipulation on the sustainable development of digital enterprises. Using data from 1200 listing digital enterprises in China and the DEA-Malmquist index method, the efficiency value of digital enterprises in 2007–2021 was estimated to represent the index of sustainable development of digital enterprises. The accounting manipulation was detected using the panel PSM-DID method based on the Administrative Measures for the Recognition of High-tech Enterprise's policy. The information accessibility value was estimated based on the MDA method. Empirical studies were conducted using text analysis, the panel PSM-DID method, and the double moderating effect model. The results showed that: (1) Accounting manipulation had a negative impact on the sustainable development of "true" digital enterprises and the "fake" digital enterprises; (2) Information accessibility directly and positively enhanced the technological progress and scale efficiency of digital enterprises, and its moderating effect was heterogeneous, with a significant moderating effect on the "true" digital enterprises and a negative effect on the "fake" ones.</p>
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International Brain Laboratory public data
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OpenNeuro
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