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Figure 3. A in Plant diversity and conservation value of wetlands along a rural-urban gradient

Figure 3. A, Total number of species per wetland site (alpha diversity); B, the average species richness per transect for each site; C, the size of each wetland; arranged along a gradient of increasing percentage urban landcover.

Figure 4. A in Plant diversity and conservation value of wetlands along a rural-urban gradient

Figure 4. A, Beta diversity between sites (calculated as the average between all the rural sites (R1–R12), the average between the two urban sites and all the rural sites (U1 and U2), and between the two urban sites (U)); B, the SIMPER analysis results of the average similarity of the transects in each wetland site; arranged along a gradient of increasing percentage urban landcover.

Figure 5. A in Plant diversity and conservation value of wetlands along a rural-urban gradient

Figure 5. A, Wetland index values (WIV) of each site; B, the average site cover descriptions; C, the percentage average growth form distribution at each site; D, the average functional diversity per site (upland (U), facultative upland (FU), facultative (F), facultative wetland (FW), obligate wetland (OB)); arranged along a gradient of increasing urban landcover.

Figure 1 in Plant diversity and conservation value of wetlands along a rural-urban gradient

Figure 1. Study area indicating the urban area of Potchefstroom, its rural surroundings and the 14 wetland study sites. Inset map shows the size and location of the urban area and Mooi River within the former Tlokwe Municipal area.

opencc-by-4.0Aug 2024View details →

Inundación en Licantén rural (Placilla)

Fotografías de la inundación en Licantén rural. Fotografías fueron facilitadas por vecinas y vecinos afectados a Gabriela Cortés, Simón Inzunza, Nikole Guerrero, Yvonee Merino y Nicolás Pérez. Fecha de entrevista para entrega de imagenes: 14 de julio de 2023

FIGURE 3 in Functional responses of stream fish communities to rural and urban land uses

FIGURE 3 | Flowchart of analysis. Biomass and dummy traits matrices were combined to produce functional diversity (FD) indices (FRic = functional richness, FEve = functional evenness, FDiv = functional divergence and FDis = functional dispersion) and community weight mean traits (CWM) matrices. The influences of land use/occupation (Soil matrix) over FD indices were evaluated through Pearson's correlations and over CWM through redundance analysis (RDA) and Pearson's correlations.

FIGURE 4 in Functional responses of stream fish communities to rural and urban land uses

FIGURE 4 | Ordination scores of community-weighted means (CWMs) of traits (gray bars) and proportions of land use/occupation (arrows: biplot scores for constraining variables along of the first principal axis of the redundancy analysis – RDA1) applied to 24 streams sampled in the state of Paraná, Brazil.

FIGURE 2 in Functional responses of stream fish communities to rural and urban land uses

FIGURE 2 | Ternary diagram of land use/occupation in the 24 streams sampled in the state of Paraná, Brazil.

FIGURE 1 in Functional responses of stream fish communities to rural and urban land uses

FIGURE 1 | Location of the sampling sites in the 24 streams in the state of Paraná, Brazil. Codes and names of streams in S1.

opencc-by-4.0Aug 2024View details →

Data from: Cross-sectional personal network analysis of adult smoking in rural areas

This data package, titled Data from: Cross-sectional personal network analysis of adult smoking in rural areas, includes several files. First, there are annonymized raw data files in .rds file format (ego_data.rds & alter_data.rds). Second, there is the R code that allow the replication of various statistical analyses. Interested parts may consult the R code as .pdf file format (Supplementary_Material_R_Code.pdf), .Rmd file format (that can be run to create the .pdf file format) and the .R file format (that can be accesed with R and RStudio). Moreover, the labels files are useful for recreating the Supplementary Material pdf file.  Readers should know that this dataset corresponds to the study (paper) Cross-sectional personal network analysis of adult smoking in rural areas.  The ego_data.rds file includes 20 variables by 76 observations (respondents) while the alter_data.rds file includes 46 variables by 1681 observations (social contacts). We collected this information by deploying a personal network analysis research design. Initially, we interviewed 83 respondents (dubbed egos). Due to missing data, we kept in the analysis 76 egos and dropped seven respondents. We recruited the respondents using a link-tracining sampling framework. We started from a number of six seeds. We interviewed the seeds then we asked them to recommend other people in the study. We continued in a referee-referral fashion until 83 interviews were completed. The study was performed in a small rural Romanian community (4124 residents): Lerești (Argeș county).  Our study was carried out in accordance with the recommendations, relevant guidelines, and regulations (specifically, those provided by the Romanian Sociologists Society, i.e., the professional association of Romanian sociologists). The research was performed in accordance with the Declaration of Helsinki. The research protocol was approved by a named institutional/licensing committee. Specifically, the Ethics Committee of the Center for Innovation in Medicine (InoMed) reviewed and approved all these study procedures (EC-INOMED Decision No. D001/09-06-2023 and No. D001/19-01-2024). All participants gave written informed consent. The privacy rights of the study participants were observed. The authors did not have access to information that could identify participants. Face-to-face interviews were collected between September 13 – 23, 2023, in Lerești, Romania. After each interview, information that could identity the participants were anonymized. Before conducting the interview, we provided each participant with a dossier containing informative materials about the project's objectives, how the data would be analyzed and reported, and their participation rights (e.g., the right to withdraw from the project at any time, even after the interview was completed). All study participants gave their written informed consent prior to enrolment in the study. The variables in the ego_data.rds file are as follows: (1) "networkCanvasEgoUUID" (unique alpha numeric code for each observation);  (2) "ego_age" (the age of each study participant);  (3) "ego_age.cen" (the age of each study participant, centered);  (4) "ego_educ_b" (the education of each ego, binary);  (5) "ego_educ_f" (the education of each ego, educational achievement);  (6) "ego_marital.s_f" (the marital status of each ego); (7) "ego_occupation.cat2_f" (the occupation of each ego);  (8) "ego_occupation_b" (the occupation of each ego, unemployed vs employed);  (9) "ego_relstatus_b" (whether the ego is in a relationship or not);  (10) "ego_sex_f" (the sex of the ego assigned at birth; male & female);  (11) "ego_sex_n" (the sex of the ego assigned at birth; 0 = male & 1 = female);   (12) "ego_smk_status_b1" (smoking status: 1 smoking, 0 others); (13) "ego_smk_status_b2" (smoking status: 1 former smoker, 0 others);   (14) "ego_smk_status_b3" (smoking status: 1 not a smoker, 0 others);  (15) "ego_smkstatus_f"  (smoking status: former smoker, never-smoker, non-smoker (smoked too little), occasional smoker, smoker);  (16) "ego_smoking_3cat"  (smoking status: non-smoker, former smoker, smoker); (17) "net.size" (number of social contacts, alters, that were elicited by an ego); (18) "net.components" (number of strong components in the personal network); (19) "net.deg.centralization" (personal network degree centralization); (20) "net.density" (personal network density).  The variables in the alter_data.rds file are as follows: (1) "alter_age" (the age of the alter);  (2) "alter_age.cen" (the age of the alter - centered);  (3) "alter_btw" (alter's betweenness score);  (4) "alter_btw.cen" (alter's betweenness score - centered);  (5) "alter_deg" (alter's degree score);  (6)  "alter_deg.cen" (alter's degree score - centered);   (7) "alter_educ_b" (alter's education);  (8) "alter_educ_f"  (alter's education);  (9) "alter_marital.s_f" (alter's marital status);  (10) "alter_relstatus_b" (alter's marital status - binary variable); (11) "alter_sex_f" (alter's sex assigned at birth); (12) "alter_sex_n" (alter's sex assigned at birth; 1 - female; 0 - male);  (13) "alter_smk_status_b1" (alter's smoking status; 1 smoker, 0 others); (14) "alter_smk_status_b2" (alter's smoking status; 1 former smoker, 0 others); (15) "alter_smk_status_b3" (alter's smoking status; 1 non-smoker, 0 others); (16) "alter_smoking_3cat" (alter's smoking status: three categories - smoker, non-smoker, former smoker); (17) "assortativity_score_fsmoker" (assortativity score for alter, former smoker); (18) "assortativity_score_nsmoker" (assortativity score for alter, non-smoker); (19) "assortativity_score_smoker" (assortativity score for alter, smoker); (20) "ego.alter_meet_f" (ego's meeting frequency with alter);  (21) "ego_alter_meet_b" (ego's meeting frequency with alter, binary variable); (22) "ego.alter_meet_n" (ego's meeting frequency with alter, numerical codes); (23) "alter_rel.w.ego_f" (type of alters in an ego's network); (24) "networkCanvasUUID" (alpha numeric code for alter); (25) "networkCanvasEgoUUID" (alpha numeric code for ego);  (26) "ego_smkstatus_f" (smoking status: former smoker, never-smoker, non-smoker (smoked too little), occasional smoker, smoker);  (27) "ego_smoking_3cat" (three categories, smoking status: former smoker, non-smoker, smoker); (28) "ego_type_fsmk" (former smoking egos by type of ego-alter relationship); (29) "ego_type_nsmk" (non smoking egos by type of ego-alter relationship); (30) "ego_type_smk" (smoking egos by type of ego-alter relationship); (31) "ego_sex_f" (ego's sex, binary); (32) "ego_sex_n" (ego's sex, numerical code, 1 female, 0 male);  (33) "ego_educ_b" (ego's education, binary variable) (34) "ego_age" (ego's age) (35) "ego_age.cen" (ego's age, centered) (36) "ego_relstatus_b" (ego's marital status, binary variable) (37) "ego_occupation_b" (ego's employment status, binary variable) (38) "net.components" (number of strong components in the personal network) (39) "net.deg.centralization" (degree centralization score in the personal networ) (40) "net.density" (density score in the personal network) (41) "prop_fsmokers" (proportion of former smokers in the personal network - alters) (42) "prop_fsmokers.cen" (proportion of former smokers in the personal network, centered- alters) (43) "prop_nsmokers" (proportion of non-smokers in the personal network- alters) (44) "prop_nsmokers.cen" (proportion of non-smokers in the personal network, centered- alters) (45) "prop_smokers" (proportion of smokers in the personal network- alters) (46) "prop_smokers.cen" (proportion of smokers in the personal network, centered- alters)

opencc-zeroJan 2024View details →

Linked collectors and determiners for: RBR herbarium - Universidade Federal Rural do Rio de Janeiro - Herbário Virtual REFLORA.

Natural history specimen data linked to collectors and determiners held within, "RBR herbarium - Universidade Federal Rural do Rio de Janeiro - Herbário Virtual REFLORA". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/e6694e54-1cd6-4527-a26e-387518ea550a">https://bionomia.net/dataset/e6694e54-1cd6-4527-a26e-387518ea550a</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/e6694e54-1cd6-4527-a26e-387518ea550a">https://gbif.org/dataset/e6694e54-1cd6-4527-a26e-387518ea550a</a>. Formatted as a Frictionless Data package.

opencc-zeroJan 2024View details →

Linked collectors and determiners for: Prey identification of free-ranging domestic cats (Felis catus) from rural and natural areas of Spain through scat analysis.

Natural history specimen data linked to collectors and determiners held within, "Prey identification of free-ranging domestic cats (Felis catus) from rural and natural areas of Spain through scat analysis". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/ef448f77-16de-4e3f-9e3c-247f4f76efff">https://bionomia.net/dataset/ef448f77-16de-4e3f-9e3c-247f4f76efff</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/ef448f77-16de-4e3f-9e3c-247f4f76efff">https://gbif.org/dataset/ef448f77-16de-4e3f-9e3c-247f4f76efff</a>. Formatted as a Frictionless Data package.

opencc-by-4.0Oct 2024View details →

Data from: Using Landsat time-series to investigate nearly 50 years of tree canopy cover change across an urban-rural landscape in southern Ontario

Paper Abstract: Canadian urban and adjacent landscapes have been dynamic over the last 50 years due to land management, land cover alternations, climate change, and disturbances. Remote sensing, particularly the Landsat archive, provides the only means to spatially quantify these long-term dynamics locally. Here, we explore the utility of Landsat, including the often-forgotten MSS sensor, for investigating percent tree canopy cover (TCC) change between 1972 and 2020 in a Canadian urban-rural context. We build a TCC time-series by training random forest models using visually interpreted TCC from high-resolution imagery. Predictors include topographic and yearly LandsatLinkr-harmonized and LandTrendr-fitted tasseled cap indices. Yearly binary TCC maps are built to mask consistently treeless areas and limit noise. To increase confidence in observed TCC change without historical reference imagery, we investigate multiple temporal validation options. Our TCC time-series (R2: 0.89, RMSE: 10.7%), quantifies TCC dynamics while limiting erroneous change and predictor space extrapolation. We explore TCC changes across landscapes, revealing periods of gain and loss associated with agricultural reforestation (1978-1996), housing development (on-going), drought (late 1990s), emerald ash borer (2010s), an ice storm (2013), and other drivers. Results demonstrate how long-term Landsat time-series can be used to better understand historical tree canopy change at local-regional scales.    Dataset details: See paper.  <ul> <li>cc_72to20.tif: Yearly tree CC predictions (1972-2020)</li> <li>always_nonforest10_nowater.tif: continuous-non-canopy mask</li> <li>water.tif: water mask</li> <li>Yearly.zip: Annual predictors (including CC10) and asc outputs</li> </ul>   See code on GitHub: <a href="https://github.com/ZZMitch/PredictTreeCC_Landsat_1972to2020">ZZMitch/PredictTreeCC_Landsat_1972to2020: Code from the portion of my PhD about using Landsat time-series to predict tree canopy cover from 1972 - 2020. Code will be released as papers are published. (github.com)</a>

opencc-by-4.0Nov 2024View details →

CHILDHOOD OBESITY IN URBAN AND RURAL INDIA: A SYSTEMATIC REVIEW AND META-ANALYSES OF PREVALENCE STUDIES

Background: Childhood obesity has become a pressing global public health issue, particularly in low- and middle-income countries like India. This systematic review aims to investigate the prevalence of childhood obesity and its associated risk factors in urban and rural regions of India. Methods: A comprehensive systematic search was conducted in PubMed, Embase, and Scopus databases to identify relevant English-language studies published within the past decade. Inclusion criteria included studies conducted in India, focusing on children and adolescents aged 0-18, and reporting either the prevalence of childhood obesity or related risk factors. Ten studies, comprising both cross-sectional and quantitative research designs, met these criteria. Results: The findings reveal a significant disparity in childhood obesity prevalence between urban and rural areas of India. Urban regions exhibit notably higher rates, with a pooled prevalence estimated at 9.0% (95% CI: 2.0 to 17), compared to 4.0% (95% CI: 4.0 to 5.0) in rural areas. Risk factors associated with childhood obesity in urban settings include unhealthy dietary habits, limited physical activity, higher income levels, parental education, and attendance at private schools. In rural areas, gender, age, and household size emerged as potential risk factors. Discussion: These findings underscore the urgent need for geographically tailored interventions to address the urban-rural disparities in childhood obesity. Lifestyle-oriented strategies promoting healthier dietary patterns and increased physical activity are essential. Gender-inclusive programs targeting both boys and girls are crucial. Future research should consider regional and cultural diversity to design more effective public health responses. Conclusion: This systematic review provides valuable insights into the prevalence and risk factors of childhood obesity in India. It highlights the necessity for customized interventions and lifestyle adjustments to combat this escalating public health challenge and reduce disparities in health outcomes.

FIG 11 in Roman graves and rural rubbish. Animal remains from the Roman cemetery of Halbturn, Austria

FIG 11. – Elements of the skeletons of two dwarf-sized dogs from the vicinity of cremation burial 248. Foto by R. Gold, Institute for Palaeontology.

FIG. 8 in Roman graves and rural rubbish. Animal remains from the Roman cemetery of Halbturn, Austria

FIG. 8. – Cattle: Relative weight distribution (%) of skeletal elements from field and grave ditches and pits, compared to a complete skeleton from the Basle collection.

FIG. 1 in Roman graves and rural rubbish. Animal remains from the Roman cemetery of Halbturn, Austria

FIG. 1. – Roman site of Halbturn: aerial and geomagnetic prospection of settlement, cemetery and field structures, with recent roads and hedges; arrow indicates excavation area with cemetery. Inserts: above – location of site in Western and Central Europe; below – Roman Austria with approximate provincial borders, river Danube and site of Halbturn.

FIG 9 in Roman graves and rural rubbish. Animal remains from the Roman cemetery of Halbturn, Austria

FIG 9. – Cattle, field ditch: schematic distribution of butchery marks; skeletal silhouettes after Helmer (1987).

opencc-by-4.0Aug 2021View details →

Pollen-insect metanetworks in rural landscapes_data and code

Data and code supporting Hall et al., "Pollen-insect interaction meta-networks identify key relationships for conservation in mosaic agricultural landscapes" Abstract Flower-visitors use different parts of the landscape through the plants they visit, however these connections vary within and among land-uses. Identifying which flower-visiting insects are carrying pollen, and from where in the landscape, can elucidate key pollen-insect interactions and identify the most important sites for maintaining community-level interactions across land-uses. We developed a bipartite meta-network, linking pollen-insect interactions with the sites they occur in, to identify which land-use types in the field where insects were collected and at the landscape-scale (within 500 m of a site), are most important for conserving pollen-insect interactions. We compared pollen-insect interactions across four different land-uses (remnant native forest, avocado orchard, dairy farm, rotational potato crop) within a mosaic agricultural landscape. We sampled insects using flight intercept traps, identified pollen carried on their bodies and quantified distinct pollen-insect interactions that were highly specialised to both natural and modified land-uses. We found that sites in crops and dairy farms had higher richness of pollen-insect interactions and higher interaction strength than small forest patches and orchards. Further, many interactions involved pollinator groups such as flies, wasps and beetles that are often under-represented in pollen-insect network studies, but were often connector species in our networks. These insect groups require greater attention to enable wholistic pollinator community conservation. Pollen samples were dominated by grass (Poaceae) pollen, indicating anemophilous plant species may provide important food resources for pollinators, particularly in modified land-uses. Field-scale land-use (within 100m of a site) better predicted pollen-insect interaction richness, uniqueness, and strength than landscape-scale. Thus, management focused at smaller scales may provide more tractable outcomes for conserving or restoring pollen-insect interactions in modified landscapes. For instance, actions aimed at linking high-richness sites with those containing unique (i.e. rare) interactions by enhancing floral corridors along field boundaries may best aid interaction diversity and connectance. The ability to map interactions across sites using a meta-network approach is practical and can inform land-use planning, whereby conservation efforts can be targeted toward areas that host key interactions between plant and pollinator species.