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Seed dispersal is a key process driving the structure, composition, and regeneration of tropical forests. Larger frugivores play a crucial role in community structuring by dispersing large seeds not dispersed by smaller frugivores. We assessed the hypothesis that brown howler monkeys (Alouatta guariba clamitans) provide seed dispersal services for a wide assemblage of plant species in both small and large Atlantic forest fragments. Although fruit availability often decreases in small fragments compared with large ones, we predicted that brown howlers are efficient seed dispersers in quantitative and qualitative terms in both forest types given their high dietary flexibility. After a 36-month study period and 2,962 sampling hours, we found that howlers swallowed and defecated intact the vast majority of seeds (96%-100%) they handled in all study sites. Overall, they defecated ca. 315,600 seeds belonging to 98 species distributed in eight growth forms. We estimated that each individual howler dispersed an average of 143 (SD = 49) seeds >2 mm per day or 52,052 (SD = 17,782) seeds per year. They dispersed seeds of 58% to 93% of the local assemblages of fleshy-fruit trees. In most cases, the richness and abundance of seed species dispersed was similar between small and large fragments. However, groups inhabiting small fragments tended to disperse a higher diversity of seeds from rarely consumed fruits than those living in large fragments. We conclude that brown howlers are legitimate seed dispersers for most fleshy-fruit species of the angiosperm assemblages of their habitats, and that they might favor the regeneration of Atlantic forest fragments with the plentiful amount of intact seeds that they disperse each year. Dataset_seeds_dispersedHere we provided data on seed dispersal by six wild groups of brown howler monkeys (Alouatta guariba clamitans). This research was conducted during a 36-month period in three small (<10 ha: S1, S2, and S3) and three large (>90 ha: L1,L2, and L3) Atlantic forest fragments in Rio Grande do Sul State, southern Brazil.Dataset_seed_handlingHere we provided data on seed/fruit handling by six wild groups of brown howler monkeys (Alouatta guariba clamitans). This research was conducted during a 36-month period in three small (<10 ha: S1, S2, and S3) and three large (>90 ha: L1,L2, and L3) Atlantic forest fragments in Rio Grande do Sul State, southern Brazil.

A Data Guide for this study is available as a web page and for download. The National Longitudinal Study of Adolescent to Adult Health (Add Health), 1994-2008 [Public Use] is a longitudinal study of a nationally representative sample of U.S. adolescents in grades 7 through 12 during the 1994-1995 school year. The Add Health cohort was followed into young adulthood with four in-home interviews, the most recent conducted in 2008 when the sample was aged 24-32. Add Health combines longitudinal survey data on respondents' social, economic, psychological, and physical well-being with contextual data on the family, neighborhood, community, school, friendships, peer groups, and romantic relationships. Add Health Wave I data collection took place between September 1994 and December 1995, and included both an in-school questionnaire and in-home interview. The in-school questionnaire was administered to more than 90,000 students in grades 7 through 12, and gathered information on social and demographic characteristics of adolescent respondents, education and occupation of parents, household structure, expectations for the future, self-esteem, health status, risk behaviors, friendships, and school-year extracurricular activities. All students listed on a sample school's roster were eligible for selection into the core in-home interview sample. In-home interviews included topics such as health status, health-facility utilization, nutrition, peer networks, decision-making processes, family composition and dynamics, educational aspirations and expectations, employment experience, romantic and sexual partnerships, substance use, and criminal activities. A parent, preferably the resident mother, of each adolescent respondent interviewed in Wave I was also asked to complete an interviewer-assisted questionnaire covering topics such as inheritable health conditions, marriages and marriage-like relationships, neighborhood characteristics, involvement in volunteer, civic, and school activities, health-affecting behaviors, education and employment, household income and economic assistance, parent-adolescent communication and interaction, parent's familiarity with the adolescent's friends and friends' parents. Add Health data collection recommenced for Wave II from April to August 1996, and included almost 15,000 follow-up in-home interviews with adolescents from Wave I. Interview questions were generally similar to Wave I, but also included questions about sun exposure and more detailed nutrition questions. Respondents were asked to report their height and weight during the course of the interview, and were also weighed and measured by the interviewer. From August 2001 to April 2002, Wave III data were collected through in-home interviews with 15,170 Wave I respondents (now 18 to 26 years old), as well as interviews with their partners. Respondents were administered survey questions designed to obtain information about family, relationships, sexual experiences, childbearing, and educational histories, labor force involvement, civic participation, religion and spirituality, mental health, health insurance, illness, delinquency and violence, gambling, substance abuse, and involvement with the criminal justice system. High School Transcript Release Forms were also collected at Wave III, and these data comprise the Education Data component of the Add Health study. Wave IV in-home interviews were conducted in 2008 and 2009 when the original Wave I respondents were 24 to 32 years old. Longitudinal survey data were collected on the social, economic, psychological, and health circumstances of respondents, as well as longitudinal geographic data. Survey questions were expanded on educational transitions, economic status and financial resources and strains, sleep patterns and sleep quality, eating habits and nutrition, illnesses and medications, physical activities, emotional content and quality of current or most recent romantic/cohabiting/marriage relationships, and maltreatment during childhood by caregivers. Dates and circumstances of key life events occurring in young adulthood were also recorded, including a complete marriage and cohabitation history, full pregnancy and fertility histories from both men and women, an educational history of dates of degrees and school attendance, contact with the criminal justice system, military service, and various employment events, including the date of first and current jobs, with respective information on occupation, industry, wages, hours, and benefits. Finally, physical measurements and biospecimens were also collected at Wave IV, and included anthropometric measures of weight, height and waist circumference, cardiovascular measures such as systolic blood pressure, diastolic blood pressure, and pulse, metabolic measures from dried blood spots assayed for lipids, glucose, and glycosylated hemoglobin (HbA1c), measures of inflammation and immune function, including High sensitivity C-reactive protein (hsCRP) and Epstein-Barr virus (EBV). Datasets: DS0: Study-Level Files DS1: Wave I: In-Home Questionnaire, Public Use Sample DS2: Wave I: Public Use Contextual Database DS3: Wave I: Network Variables DS4: Wave I: Public Use Grand Sample Weights DS5: Wave II: In-Home Questionnaire, Public Use Sample DS6: Wave II: Public Use Contextual Database DS7: Wave II: Public Use Grand Sample Weights DS8: Wave III: In-Home Questionnaire, Public Use Sample DS9: Wave III: In-Home Questionnaire, Public Use Sample (Section 17: Relationships) DS10: Wave III: In-Home Questionnaire, Public Use Sample (Section 18: Pregnancies) DS11: Wave III: In-Home Questionnaire, Public Use Sample (Section 19: Relationships in Detail) DS12: Wave III: In-Home Questionnaire, Public Use Sample (Section 22: Completed Pregnancies) DS13: Wave III: In-Home Questionnaire, Public Use Sample (Section 23: Current Pregnancies) DS14: Wave III: In-Home Questionnaire, Public Use Sample (Section 24: Live Births) DS15: Wave III: In-Home Questionnaire, Public Use Sample (Section 25: Children and Parenting) DS16: Wave III: Public Use Education Data DS17: Wave III: Public Use Graduation Data DS18: Wave III: Public Use Education Data Weights DS19: Wave III: Add Health School Weights DS20: Wave III: Peabody Picture Vocabulary Test (PVT), Public Use DS21: Wave III: Public In-Home Weights DS22: Wave IV: In-Home Questionnaire, Public Use Sample DS23: Wave IV: In-Home Questionnaire, Public Use Sample (Section 16B: Relationships) DS24: Wave IV: In-Home Questionnaire, Public Use Sample (Section 16C: Relationships) DS25: Wave IV: In-Home Questionnaire, Public Use Sample (Section 18: Pregnancy Table) DS26: Wave IV: In-Home Questionnaire, Public Use Sample (Section 19: Live Births) DS27: Wave IV: In-Home Questionnaire, Public Use Sample (Section 20A: Children and Parenting) DS28: Wave IV: Biomarkers, Measures of Inflammation and Immune Function DS29: Wave IV: Biomarkers, Measures of Glucose Homeostasis DS30: Wave IV: Biomarkers, Lipids DS31: Wave IV: Public Use Weights Wave I: The Stage 1 in-school sample was a stratified, random sample of all high schools in the United States. A school was eligible for the sample if it included an 11th grade and had a minimum enrollment of 30 students. A feeder school -- a school that sent graduates to the high school and that included a 7th grade -- was also recruited from the community. The in-school questionnaire was administered to more than 90,000 students in grades 7 through 12. The Stage 2 in-home sample of 27,000 adolescents consisted of a core sample from each community, plus selected special over samples. Eligibility for over samples was determined by an adolescent's responses on the in-school questionnaire. Adolescents could qualify for more than one sample.; Wave II: The Wave II in-home interview surveyed almost 15,000 of the same students one year after Wave I.; Wave III: The in-home Wave III sample consists of over 15,000 Wave I respondents who could be located and re-interviewed six years later.; Wave IV: All original Wave I in-home respondents were eligible for in-home interviews at Wave IV. At Wave IV, the Add Health sample was dispersed across the nation with respondents living in all 50 states. Administrators were able to locate 92.5% of the Wave IV sample and interviewed 80.3% of eligible sample members. ; For additional information on sampling, including detailed information on special oversamples, please see the Add Health Study Design page. Add Health was developed in response to a mandate from the U.S. Congress to fund a study of adolescent health. Waves I and II focused on the forces that may influence adolescents' health and risk behaviors, including personal traits, families, friendships, romantic relationships, peer groups, schools, neighborhoods, and communities. As participants aged into adulthood, the scientific goals of the study expanded and evolved. Wave III explored adolescent experiences and behaviors related to decisions, behavior, and health outcomes in the transition to adulthood. Wave IV expanded to examine developmental and health trajectories across the life course of adolescence into young adulthood, using an integrative study design which combined social, behavioral, and biomedical measures data collection. Response Rates: Response rates for each wave were as follows: Wave I: 79 percent; Wave II: 88.6 percent; Wave III: 77.4 percent; Wave IV: 80.3 percent; Adolescents in grades 7 through 12 during the 1994-1995 school year. Respondents were geographically located in the United States. audio computer-assisted self interview (ACASI) computer-assisted personal interview (CAPI) computer-assisted self interview (CASI) paper and pencil interview (PAPI) face-to-face interview

Global trade (physical trade balances) with cereals, oil crops and meat from 1850/70 to 2016 by world regions; Global sown area, production and yield per unit are of wheat; Global cereal export per capita of global population.

Background: Paired with satellite location telemetry, animal-borne instruments can collect spatiotemporal data describing the animal’s movement and environment at a scale relevant to its behavior. Ecologists have developed methods for identifying the area(s) used by an animal (e.g., home range) and those used most intensely (utilization distribution) based on location data. However, few have extended these models beyond their traditional roles as descriptive 2D summaries of point data. Here we demonstrate how the home range method, T-LoCoH, can be expanded to quantify collective sampling coverage by multiple instrumented animals using grey seals (Halichoerus grypus) equipped with GPS tags and acoustic transceivers on the Scotian Shelf (Atlantic Canada) as a case study. At the individual level, we illustrate how time and space-use metrics quantifying individual sampling coverage may be used to determine the rate of acoustic transmissions received. Results: Grey seals collectively sampled an area of 11,308 km 2 and intensely sampled an area of 31 km 2 from June-December. The largest area sampled was in July (2094.56 km 2 ) and the smallest area sampled occurred in August (1259.80 km 2 ), with changes in sampling coverage observed through time. Conclusions: T-LoCoH provides an effective means to quantify changes in collective sampling effort by multiple instrumented animals and to compare these changes across time. We also illustrate how time and space-use metrics of individual instrumented seal movement calculated using T-LoCoH can be used to account for differences in the amount of time a bioprobe (biological sampling platform) spends in an area. Baker L, Flemming JEM, Jonsen ID, Lidgard DC, Iverson SJ, Bowen WD (2015) A novel approach to quantifying the spatiotemporal behavior of instrumented grey seals used to sample the environment. Movement Ecology 3(1):20. doi:10.1186/s40462-015-0047-4

1. Site moisture is an important component of the forest landscape for maintaining biodiversity, including forest-floor bryophytes, but little is known about its role in shaping understory responses to harvesting. 2. We investigated the influence of site wetness, determined using a remotely-sensed, topographic depth-to-water (DTW) index, on responses of bryophyte cover, richness, diversity, and composition to variable retention harvesting (comparing: 2% [clear-cut], 20%, and 50% dispersed green tree retention and uncut controls [100% retention]) in three boreal forest cover-types (broadleaf, mixed, and conifer forests) in western Canada. The DTW index provides an approximation of depth to water at or below the soil surface, and was derived from wet-areas mapping based on discrete Airborne Laser Scanning data acquired over an experimentally harvested landscape located in northwestern Alberta, Canada. 3. The effectiveness of leaving retention (versus clear-cutting) for conserving bryophyte communities depended on site wetness, as indicated by DTW, with the specifics varying among forest types. In broadleaf forests, bryophyte cover and richness were generally low and not much affected by harvesting but drier sites had higher richness and a few more unique species. In mixed and conifer forests, leaving retention (versus clear-cutting) on wetter (versus drier) sites was more effective for conserving bryophyte cover, wetter sites had higher total species richness, and more species were exclusive to wetter sites. 4. Synthesis and applications. Site wetness, as indicated using the remotely-sensed topographic site wetness index "depth-to-water," mediates bryophyte responses to variable-retention harvests. Specifically, our results suggested that in conifer and mixed forests it would be more beneficial to target wetter sites for retention patches or dispersed retention whereas in broadleaf sites there might be a slight advantage to targeting drier sites. Our study demonstrates that this tool could be used to inform management decisions around leaving dispersed or patch retention.28-Jan-2019 Bryophyte species and depth-to-water index valuesBryophyte (mosses and liverworts) species cover data and estimation of depth-to-water index values for retention harvest sites sampled in northwestern Alberta, Canada.Bartels-et-al-2019-deposited data-Dryad.xlsx

This file describes the data files and execution files needed to recreate tables iii-vi in the paper and all of the figures and tables presented in the online appendix.

Instructions for Matlab code and main result figures: 1- Download all data files and Matlab functions (see requirements) and ensure they are all in the same directory. 2- Open SourceCode_GroupFigures_RasmanEtAl_Elife2021.m with Matlab. 3- Make sure Matlab is currently in the folder where you put the files or add that folder to the path. 4- Run the code. All group result figures will be generated. Matlab will output warning when running the exponential fit procedure, but this is expected for the code. Instructions for LabVIEW code: 1- Download .vi file and open with compatible LabVIEW software. Download associated sampledummydata to be used with LabVIEW vi. 2- View annotated instructions in LabVIEW front panel. 3- Load sample data and run program. Requirements: Matlab toolboxes required: curve fitting toolbox, statistics and machine learning toolbox For several figures, hline and vline functions will be needed for plotting. These functions are available at https://www.mathworks.com/matlabcentral/fileexchange/1039-hline-and-vline REFERENCE: Brandon Kuczenski (2021). hline and vline (https://www.mathworks.com/matlabcentral/fileexchange/1039-hline-and-vline), MATLAB Central File Exchange. Retrieved August 1, 2021. For Figure 4, boxplotgroup function is needed for plotting. This function can be downloaded at https://www.mathworks.com/matlabcentral/fileexchange/74437-boxplotgroup REFERENCE: Adam Danz (2021). boxplotGroup (https://www.mathworks.com/matlabcentral/fileexchange/74437-boxplotgroup), MATLAB Central File Exchange. Retrieved August 1, 2021. Please reference this work using: Data and code: Rasman BG, Forbes PA, Peters RM, Ortiz O, Franks I, Inglis JT, Chua R, and Blouin JS. 2021, "Data and code for "Learning to stand with unexpected sensorimotor delays", DOI: https://doi.org/10.5683/SP2/IKX9ML, Scholars Portal Dataverse Paper: Rasman BG, Forbes PA, Peters RM, Ortiz O, Franks I, Inglis JT, Chua R, and Blouin JS. Learning to stand with unexpected sensorimotor delays. eLife. 2021: e65085. DOI: https://doi.org/10.7554/eLife.65085 These files consist of data and Matlab code needed to reproduce the main result figures from Experiments 1, 2 and 3 of "Learning to stand with unexpected sensorimotor delays". Additionally, LabVIEW code is provided to produce robust Bayesian fits for perceptual data. Data and results include: standing balance behavior (sway velocity variance, percent time within balancing limits) with imposed delays, vestibular-evoked muscle responses (coherence, gain, cross-covariance) when standing with imposed delays, and perceptual thresholds to detecting unexpected standing motion when standing with imposed delays. Data are provided in spreadsheets (for viewing purposes) and also in .mat matlab files (to run with source code).

Nigeria is estimated to account for 19% of all estimated global maternal deaths with approximately 58,000 in 2015. The high number is partly due to the inadequate access of women to evidence-based skilled pregnancy care. The Federal Ministry of Health (FMoH) and all major health policy agencies in Nigeria have recognized the need for increased access to skilled obstetric care, especially in rural areas, as critical to reducing the high rate of maternal mortality. However, despite the fact that policymakers recognize that primary health care should play a key role in improving rural women's access to skilled pregnancy care, Primary Health Centres (PHCs) are often poorly utilized throughout the country. This project is a 5-year (2015-2020) implementation research conducted by the Women's Health and Action Research Centre (WHARC), Benin City, Nigeria in collaboration with the University of Ottawa (UOttawa), Canada and with funding from the International Development Research Centre (IDRC), Global Affairs Canada (GAC) and the Canadian Institute for Health Research (CIHR) under the Innovating for Maternal and Child Health in Africa (IMCHA) Initiative. The project's specific objectives are: 1) to identify the demand and supply factors responsible for the use and non-use of PHCs for pregnancy care in Esan South East and Etsako East LGAs of Edo State, Nigeria; 2) based on Objective 1, to derive and implement a set of multi-faceted community-led interventions to increase women's access to skilled pregnancy care offered in PHCs in Esan South East and Etsako East Local Government Areas (LGA); and 3) to evaluate the effectiveness of the interventions using both indicators of access to services, as well as maternal and fetal/newborn health outcomes in the intervention communities. The study was conducted in Esan South East and Etsako East Local Government Areas (LGAs) in Edo State in southern Nigeria. Both LGAs are located in the rural and riverine areas of the state, adjacent to River Niger, with Estako East in the northern part of the Edo State part of the river, while Esan South East is in the southern part. Edo State is one of Nigeria’s thirty-six states. Each state consists of LGAs, and LGAs consist of political/health Wards. The study was originally designed to be a randomized control trial (Yaya et al., 2018) but was changed to a quasi-experiment separate sample pretest and posttest design. The change was necessitated by the difficulty in achieving reliable randomization in the study communities. The study was conducted in three phases. At phase one, a baseline was conducted using a mixed-method approach to address objective 1. Based on the results of the baseline research, a set of intervention activities were designed and implemented simultaneously in phase 2 for two years. Phase three was the endline research which addressed the study objective 3. Ethical approval for the study was obtained from the National Health Research Ethics Committee (NHREC) of Nigeria – protocol number NHREC/01/01/2007 – 10/04/2017; and written informed consent was obtained from individual respondent/participant, except in the community conversations where the consent was verbal. The data we are sharing contain baseline and endline data. collected through a mixed-method approach to address the study objectives. The baseline data were collected between July 29 to August 16, 2017, using a mixed-method that comprises a household survey, exit interview, PHC site assessment survey, community conversation, focus group discussion, and key informant interview. The endline data were collected between June 24 and July 6, 2020, using a household survey. All the data collection instruments were pretested and the data were collected by trained data collectors. Response Rates: The sample size for the baseline and end household survey was 1,318, to adjust for non-response, 10% was added to derive a total of 1,450. At baseline, 1408 responded, and at endline 1,411 responded. Based on replacement of non-response, the total number expected were covered during the two surveys bringing the response rate to be 100% Household survey: Multistage, systematic, random sampling design;Exit Interview: All eligible women were interviewed;Site Assessment survey: Random sampling;Qualitative data: Purposive and convenient sampling Ever married women age 15-45 years oldPrimary Health Centres. Smallest Geographic Unit: Local Government Area computer-assisted personal interview (CAPI); face-to-face interview;

Simulated Raman scattering spectra produced by non-linear pumping of a single-mode optical fiber. Here the input modulation is slowed to 2 Hz so that the changes to the output spectra can be seen.

Trajectories of plant communities can be described by different models of plant succession. While a Clementsian (gradual continuum model) or Gleasonian approach (relay floristics model) has traditionally been used to inform restoration outcomes, alternative succession models developed recently may better represent restoration trajectories. The threshold dynamics succession model, which predicts an abrupt species turnover after an environmental threshold is crossed, has never been used in a restoration context. This model might, however, better describe shifts in plant competitive ranking and facilitation interactions during species turnover. Fifty-three riparian zones, planted with trees 3–17 years prior to sampling, and 14 natural riparian forests were studied in two agricultural watersheds of south-eastern Québec (Canada). The cover of vegetation strata was assessed at the site scale, and the cover of plant species was estimated in a total of 784 1-m2 plots. Canopy cover was measured stereoscopically for each plot. As revealed by Principal Response Curves and broken stick models, herbaceous species composition was stable during the first 12–13 years after tree planting, but then abruptly shifted. This two-step pattern in species turnover followed the increase in canopy cover after tree planting. Once canopy cover passed a threshold of ca 40%, plant succession started and led to the re-establishment of forest communities 17 years after planting. Following herbaceous species turnover, the cover of ecological groups changed significantly towards covers of natural riparian forests: shade-tolerant species generally increased, while light-demanding and non-native species decreased. Vegetation structure was also significantly affected by tree planting: tree and shrub cover increased, while monocot cover decreased. Synthesis and applications. Tree planting efficiently restored herbaceous forest communities in riparian zones by inducing a species turnover mediated by light availability corresponding to the threshold dynamics model in plant succession. Fostering and monitoring canopy closure in tree-planted riparian zones should improve restoration success and the design of alternative strategies. The innovative statistical approach of this study aiming to identify succession patterns and their associated theoretical models can guide future restoration in any type of ecosystem around the world to bridge the gap between science and management. Data_Bourgeois_et_al_2016_JApplEcol