• 2015-2024close
• Open Access close
• Restricted close
• Research data close
• Research software close
• Other research products close
• Natural Sciences and Engineering Re... close
• CA close
• English close

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. Although anthropogenic edges are an important consequence of timber harvesting, edges due to natural disturbances or landscape heterogeneity are also common. Forest edges have been well-studied in temperate and tropical forests, but less so in less productive, disturbance-adapted boreal forests. 2. We synthesized data on forest vegetation at edges of boreal forests and compared edge influence among edge types (fire, cut, lake/wetland; old vs. young), forest types (broadleaf vs. coniferous) and geographic regions. Our objectives were to quantify vegetation responses at edges of all types and to compare the strength and extent of edge influence among different types of edges and forests. 3. Research was conducted using the same general sampling design in Alberta, Ontario and Quebec in Canada, and in Sweden and Finland. We conducted a meta-analysis for a variety of response variables including forest structure, deadwood abundance, regeneration, understorey abundance and diversity, and nonvascular plant cover. We also determined the magnitude and distance of edge influence using randomization tests. 4. Some edge responses (lower tree basal area, tree canopy and bryophyte cover; more logs; higher regeneration) were significant overall across studies. Edge influence on ground vegetation in boreal forests was generally weak, not very extensive (distance of edge influence usually < 20 m) and decreased with time. We found more extensive edge influence at natural edges, at younger edges and in broadleaf forests. The comparison among regions revealed weaker edge influence in Fennoscandian forests. 5. Synthesis. Edges created by forest harvesting do not appear to have as strong, extensive or persistent influence on vegetation in boreal as in tropical or temperate forested ecosystems. We attribute this apparent resistance to shorter canopy heights, inherent heterogeneity in boreal forests and their adaptation to frequent natural disturbance. Nevertheless, notable differences between forest structure responses to natural (fire) and anthropogenic (cut) edges raise concerns about biodiversity implications of extensive creation of anthropogenic edges. By highlighting universal responses to edge influence in boreal forests that are significant irrespective of edge or forest type, and those which vary by edge type, we provide a context for the conservation of boreal forests. Data for meta-analysis and synthesis of boreal edgesData from each study is on a separate page, labelled with the study area and study number. Please see the article Table 2. On each page, data are at different distances from the edge along transects for different response variables. Please see the article Table S1 for details on sampling and data collection.Boreal edges data for Dryad.xls

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).

The performance of courtship signals provides information about the behavioural state and quality of the signaller, and females can use such information for social decision-making (e.g. mate choice). However, relatively little is known about the degree to which the perception of and preference for differences in motor performance are shaped by developmental experiences. Furthermore, the neural substrates that development could act upon to influence the processing of performance features remains largely unknown. In songbirds, females use song to identify males and select mates. Moreover, female songbirds are often sensitive to variation in male song performance. Consequently, we investigated how developmental exposure to adult male song affected behavioural and neural responses to song in a small, gregarious songbird, the zebra finch. Zebra finch males modulate their song performance when courting females, and previous work has shown that females prefer the high-performance, female-directed courtship song. However, unlike females allowed to hear and interact with an adult male during development, females reared without developmental song exposure did not demonstrate behavioural preferences for high-performance courtship songs. Additionally, auditory responses to courtship and non-courtship song were altered in adult females raised without developmental song exposure. These data highlight the critical role of developmental auditory experience in shaping the perception and processing of song performance. EGR1_dataNumber of EGR1 neurons/mm2 in the NCM, CMM and IC.preference_score_by_maleIDAverage preference scores of all females tested on each male stimulus.preference_scores_all_femalesraw data for call back preference tests for normally-reared and song-naive females tested on stimuli from different malespreference_score_vs_song_measuresPercent difference for measures of song between courtship and non-courtship singing. Measures include the number of introductory notes and motifs, syllable entropy, CV of the fundamental frequency and song tempo (motif duration).

Active sensing has been well documented in animals that use echolocation and electrolocation. Active photolocation, or active sensing using light, has received much less attention, and only in bioluminescent nocturnal species. However, evidence has suggested the diurnal triplefin Tripterygion delaisi uses controlled iris radiance, termed ocular sparks, for prey detection. While this form of diurnal active photolocation was behaviourally described, a study exploring the physical process would provide compelling support for this mechanism. In this paper, we investigate the conditions under which diurnal active photolocation could assist T. delaisi in detecting potential prey. In the field, we sampled gammarids (genus Cheirocratus) and characterized the spectral properties of their eyes, which possess strong directional reflectors. In the laboratory, we quantified ocular sparks size and their angle-dependent radiance. Combined with environmental light measurements and known properties of the visual system of T. delaisi, we modeled diurnal active photolocation under various scenarios. Our results corroborate that diurnal active photolocation should help T. delaisi detect gammarids at distances relevant to foraging, 4.5 cm under favourable conditions and up to 2.5 cm under average conditions. To determine the prevalence of diurnal active photolocation for micro-prey, we encourage further theoretical and empirical work. Average gammarid body reflectanceSpectrophotometric data for body reflectance of Cheirocratus gammaridsAverage gammarid body transmissionSpectrophotometric transmission measurements of Cheirocratus gammarid bodyBackground reflectance Haliopteris filicianaSpectrophotometric measurements of Haliopteris filicianaCoaxial reflectance values categoricalReflective properties of Gammarid eyes measured with coaxial light sourceDownwelling and sidewelling illuminant for analysesDownwelling and sidewelling light fieldsEye reflectance conversion values categoricalConversion factors to produce non-coaxial reflectance values from co-axial reflectance values for gammarid eyesOcular media valuesTransmission properties of the ocular media of Tripterigyon delaisiOcular spark conversion on continuous scaleConversion curves for transforming downwelling irradiance into ocular spark radianceSA scores Gammarid as perceived by Td pupilSolid angle of the gammarid eye as perceived from the pupil of T. delaisi based on the interaction distanceSA scores Os as perceived by Gammarid eye MATRIXSolid angles of the Ocular spark from T delaisi as perceived by the gammarid eye based on the interaction distance

The physiological mechanisms underlying local adaptation in natural populations of animals, and whether the same mechanisms contribute to adaptation and acclimation, are largely unknown. Therefore, we tested for evolutionary divergence in aerobic exercise physiology in laboratory bred, size-matched crosses of ancestral, benthic, normal Lake Whitefish (Coregonus clupeaformis) and derived, limnetic, more actively-swimming ‘dwarf’ ecotypes. We acclimated fish to constant swimming (emulating limnetic foraging) and control conditions (emulating normal activity levels) to simultaneously study phenotypic plasticity. We found extensive divergence between ecotypes: dwarf fish generally had constitutively higher values of traits related to oxygen transport (ventricle size) and use by skeletal muscle (percent oxidative muscle, mitochondrial content), and also evolved differential plasticity of mitochondrial function (Complex I activity and flux through Complexes I-IV and IV). The effects of swim-training were less pronounced than differences among ecotypes and the traits which had a significant training effect (ventricle protein content, ventricle MDH activity and muscle Complex V activity) did not differ among ecotypes. Only one trait, ventricle mass, varied in a similar manner with acclimation and adaptation and followed a pattern consistent with genetic accommodation. Overall, the physiological and biochemical mechanisms underlying acclimation and adaptation to swimming activity in Lake Whitefish generally differ. R code for nested, two-way ANOVAsR code for nested, two-way ANOVAs (example for Fig. 1A)Figs1-6_MixedEffectsModel_Code.RR_Code_for_DFAR_Code_for_DFA (Fig. 7)Fig7_DFA_Code.RFig1A_HematocritData for Figure 1AFig1B_VentricleMassData for Figure 1BFig2_HeartEnzymesData for Figure 2Fig3A_PercentRMData for Figure 3AFig3C,E_CapillaryDensityData for Figure 3C,EFig4_MuscleEnzymesData for Figure 4Fig5_MitoRespirationData for Figure 5Fig6_ETCenzymesData for Figure 6Fig7_DF(tank_means)Data for Figure 7 - tank means for all significant variables

This dataset contains methane and nitrous oxide dissolved gas concentration, dissolved methane carbon isotope, and ancillary hydrographic data from research cruises in the North American Arctic Ocean between 2015-2018. Ocean samples for methane and nitrous oxide analysis were collected from Niskin bottles mounted on a CTD rosette. Water was collected into glass serum bottles and allowed to overflow three times before preserving with mercuric chloride and sealing with with butyl rubber stoppers and aluminum crimp seals. Gas concentrations were determined using a purge and trap system coupled to a gas chromatograph/mass spectrometer, following the method of Capelle et al. (2015). Equilibrium dry atmospheric concentrations were 328.25, 329.14, 330.11, and 330.96 ppb for N2O and 1919.64, 1933.67, 1934.92, and 1933.50 ppb for CH4 in 2015, 2016, 2017, and 2018, respectively. Equilibrium dissolved concentrations were calculated from the measured temperature and salinity following Wiesenburg and Guinasso (1979) for CH4 and Weiss and Price (1980) for N2O. Equilibrium concentrations were calculated based on sample temperature and salinity and the atmospheric N2O or CH4 concentrations measured at Barrow, Alaska by the NOAA Earth System Research Laboratory Global Monitoring Division (Dlugokencky et al., 2020a,b), with corrections to local sea level pressure and 100% humidity. Oxygen concentration was determined using an oxygen sensor mounted on the Niskin rosette, calibrated with discrete samples analyzed by Winkler titration. The mixed layer depth was defined based on a potential density difference criterion of 0.125 kg/m³ relative to the density at 5 m depth, using CTD profiles binned to 1 m. The mixed layer depth was set to 5 m as a minimum. The instantaneous gas transfer velocities and fluxes are based on the instantaneous wind speed at the time of sampling. The 30-day weighted gas transfer velocities and fluxes are integrated over the residence time of the gas in the mixed layer, using up to the prior 30 days of observations, following the method of Teeter et al. (2018) as described in the main manuscript of Manning et al. (2022). The 60-day weighted gas transfer velocities and fluxes are integrated over the residence time of the gas in the mixed layer, using the prior 60 days of observations, following the method of Teeter et al. (2018) as described in the main manuscript of Manning et al. (2022). Atmospheric sea level pressure was obtained from the NCEP/NCAR reanalysis product, which is provided by the NOAA-ESRL Physical Sciences Laboratory (https://psl.noaa.gov/data/gridded). Fractional ice cover was obtained from the EUMETSAT Ocean and Sea Ice Satellite Application Facility (https://osi-saf.eumetsat.int). Sea ice concentration product AMSR-2 (identifier OSI-408) was used in 2017–2018 and SSMIS (identifier OSI-401-b) was used in 2015–2016.

There is currently conflict in the literature on the taxonomic status of the reportedly cosmopolitan species Neosiphonia harveyi, a common red alga along the coast of Atlantic Canada and New England, USA. Neosiphonia harveyi sensu lato was assessed using three molecular markers: COI-5P, ITS and rbcL. All three markers clearly delimited three genetic species groups within N. harveyi sensu lato in this region, which we identified as N. harveyi, N. japonica and Polysiphonia akkeshiensis (here resurrected from synonymy with N. japonica). Although Neosiphonia harveyi is considered by some authors to be introduced to the Atlantic from the western Pacific, it was only confirmed from the North Atlantic suggesting it is native to this area. In contrast, Neosiphonia japonica was collected from only two sites in Rhode Island, USA, as well as from its reported native range in Asia (South Korea), which when combined with data in GenBank indicates that this species was introduced to the Northwest Atlantic. The GenBank data further indicate that N. japonica was also introduced to North Carolina, Spain, Australia and New Zealand. Despite the fact that all three markers clearly delimited N. harveyi and N. japonica as distinct genetic species groups, the ITS sequences for some N. harveyi individuals displayed mixed patterns and additivity indicating introgression of nuclear DNA from N. japonica into N. harveyi in the Northwest Atlantic. Introgression of DNA from an introduced species to a native species (i.e. “genetic pollution”) is one of the possible consequences of species introductions, and we believe this is the first documented evidence for this phenomenon in red algae. ITS sequence alignmentAn alignment of ITS sequences that were used to create a neighbor-joining tree for Figure 1COI-5P sequence alignmentAn alignment of COI-5P sequences that were used to create a neighbor-joining tree for Figure 1rbcL sequence alignmentAn alignment of rbcL sequences that were used to create a neighbor-joining tree for Figure 3Figure 3 rbcL treeA neighbor-joining tree generated from rbcL sequence dataFigure 1 COI-5P treeA neighbor-joining tree generated from COI-5P sequence dataFigure 1 ITS treeA neighbor-joining tree generated from ITS sequence data

1.The question of when to monitor and when to act is fundamental to applied ecology, and notoriously difficult to answer. Value of information (VOI) theory holds great promise to help answer this question for many management problems. However, VOI theory in applied ecology has only been demonstrated in single-decision problems, and has lacked explicit links between monitoring and management costs. 2.Here, we present an extension of VOI theory for solving multi-unit decisions of whether to monitor before managing, while explicitly accounting for monitoring costs. Our formulation helps to choose the optimal monitoring/management strategy among groups of management units (e.g. species, habitat patches), and can be used to examine the benefits of partial and repeat monitoring. 3.To demonstrate our approach, we use case simulated studies of single-species protection that must choose among potential habitat areas, and classification and management of multiple species threatened with extinction. We provide spreadsheets and code to illustrate the calculations and facilitate application. Our case studies demonstrate the utility of predicting the number of units with a given outcome for problems with probabilities of discrete states, and the efficiency of having a flexible approach to manage according to monitoring outcomes. 4.Synthesis and applications. The decision to act or gather more information can have serious consequences for management. No decision, including the decision to monitor, is risk-free. Our multi-unit expansion of Value of Information (VOI) theory can reduce the risk in monitoring/acting decisions for many applied ecology problems. While our approach cannot account for the potential value of discovering previously unknown threats or ecological processes via monitoring programs, it can provide quantitative guidance on whether to monitor before acting, and which monitoring/management actions are most likely to meet management objectives. Multi-unit VOI functionsCode to simulate and analyze data for multi-unit value of information (VOI) problems in Bennett et al. (J. Appl. Ecol.)voi functions multi unit.txt