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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.

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

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).

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.

Aim: The influence of humans on large carnivores, including wolves, is a worldwide conservation concern. In addition, human‐caused changes in carnivore density and distribution might have impacts on prey and, indirectly, on vegetation. We therefore tested wolf responses to infrastructure related to natural resource development (i.e., human footprint). Location: Our study provides one of the most extensive assessments of how predators like wolves select habitat in response to various degrees of footprint across boreal ecosystems encompassing over a million square kilometers of Canada. Methods: We deployed GPS‐collars on 172 wolves, monitored movements and used a generalized functional response (GFR) model of resource selection. A functional response in habitat selection occurs when selection varies as a function of the availability of that habitat. GFRs can clarify how human‐induced habitat changes are influencing wildlife across large, diverse landscapes. Results: Wolves displayed a functional response to footprint. Wolves were more likely to select forest harvest cutblocks in regions with higher cutblock density (i.e., a positive functional response to high‐quality habitats for ungulate prey) and to select for higher road density in regions where road density was high (i.e., a positive functional response to human‐created travel routes). Wolves were more likely to use cutblocks in habitats with low road densities, and more likely to use roads in habitats with low cutblock densities, except in winter when wolves were more likely to use roads regardless of cutblock density. Main conclusions: These interactions suggest that wolves trade‐off among human‐impacted habitats, and adaptively switch from using roads to facilitate movement (while also risking encounters with humans), to using cutblocks that may have higher ungulate densities. We recommend that conservation managers consider the contextual and interacting effects of footprints when assessing impacts on carnivores. These effects likely have indirect impacts on ecosystems too, including on prey species. Functional response of wolves to human development across boreal North AmericaBoreal wolf RSF raster fileswolf_rasters.zip

The USask OMPS-LP L2 2D Ozone v1.1 product provides ozone profile retrievals performed at the University of Saskatchewan for the central slit of the Ozone Mapping and Profiler Suite Limb Profiler (OMPS-LP) instrument on the Suomi-NPP satellite. The two-dimensional retrieval algorithm accounts for variation in the along orbital track dimension, retrieving an entire orbit simultaneously instead of treating each image independently. Ozone is retrieved from the thermal tropopause to 59 km on a 1 km grid with a vertical resolution of approximately 2 km. Each granule contains data from the daylight portion of each orbit measured for a full month. Spatial coverage is global (-82 to +82 degrees latitude), and there are about 14.5 orbits per day, each has typically 160 profiles with an along orbital track sampling of 125 km. The files are written using NetCDF4. Global coverage

The dataset includes 45 natural forest scan clips with manually labelled classes (1: stem, 2: branch, 3: other).

Live feedback of vasculature in this region acquired during manual depth scanning.

In each site (1200 m²), three circular plots (400 m²) were established (total of 198 plots). Data presented here for 1) plant of the herb layer and 2) shrub and tree are grouped by site (addition of three plots). In the herb layer, total plant species identity and abundance (percentage cover) were sampled in each plot using eight circular micro-plots of 4 m². Data were collected in 2016 and 2017, between June and August. To minimize seasonal variability and allow detection of early spring species, plants (identity and abundance) in the herb layer were measured twice (once in June to early-July, and once in late-July to August). For tree, in each plot, species identity, diameter at breast height (DBH, 1.3 m) and locations of each tree > 9.1 cm DBH were determined. In each plot, species identity and DBH of shrubs and small trees (DBH range: 1.1 to 9.1 cm) were measured in three circular micro-plots of 25 m². Data that were related to the shrub-canopy layer included all trees and shrubs with DBH > 1.1 cm. Here, in each site, forest composition and structure is represented by different combinations of DBH classes (1.1-4 cm, 4-9.1 cm, 9.1-20 cm, 20-35 cm, >35 cm) and species. Plant community composition and abundance were assessed in unmanaged forests (sites of old-growth forest > 100 years, with dominant and co-dominant trees older than 200 years, and no obvious sign of past harvesting), and in even-aged and uneven-aged managed forests along a chronosequence (< 5 years, 15 years, 30 years after forest harvesting).