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Data and code from Rodkey et al. (2024): "Sod farms drive habitat selection of a migratory grassland shorebird during a critical stopover period"
<p>The data and code in this repository was used to fit the final models for the submitted manuscript "Sod farms drive habitat selection of a migratory grassland shorebird" by Rodkey and coauthors.</p> <p><strong>Abstract:</strong></p> <p>Migratory shorebirds are one of the fastest declining groups of North American avifauna. Yet, relatively little is known about how these species select habitat during migration. We explored the habitat selection of Buff-breasted Sandpipers (<em>Calidris subruficollis</em>) during spring and fall migration through the Texas Coastal Plain, a major stopover region for this species. Using tracking data from 118 birds compiled over 4 years, we found Buff-breasted Sandpipers selected intensively managed crops such as sod and short-stature crop fields, but generally avoided rangeland and areas near trees and shrubs. This work supports prior studies that also indicate the importance of short-stature vegetation for this species. Use of sod and corn varied by season, with birds preferring sod in spring, and avoiding corn when it is tall, but selecting for corn in fall after harvest. This dependence on cropland in the Texas Coastal Plain is contrary to habitat use observed in other parts of their non-breeding range, where rangelands are used extensively. The species' almost complete reliance on a highly specialized crop, sod, at this critical stopover site raises concerns about potential exposure to contaminants as well as questions about whether current management practices are providing suitable conditions for migratory grassland birds.</p> <p> </p> <p><strong>Data and code for model fitting </strong><em>bbsa_sodfarm_selection.zip</em><br>Following files are used to fit final models for the Texas study and the range-wide study.</p> <p><strong>script_tx </strong>is the script used to fit models on Texas dataset. <strong> </strong><em>script_tx.R</em></p> <p><strong>script_rw </strong> is the script used to fit models on range-wide dataset. <em>script_rw.R</em></p> <p><strong>data_tx </strong>is the datafile for containing model input data for the Texas models. data<em>_tx.csv</em></p> <p><strong>data_rw </strong>is the datafile containing model input data for the rangewide models. <em>data_rw.csv</em></p> <p>Columns in the datafiles for both Texas and rangewide models (data_tx and data_rw) are organized as follows:</p> <p>ID - unique identifier for tracked birds</p> <p>year - year in which bird was tracked</p> <p>season - season in which bird was tracked (spring or fall)</p> <p>fromTexas - (rangewide dataset only) whether or not the tracked bird was captured in the study region, the Texas Coastal Plain (T = bird was caught within study region; F = bird was caught outside of study region)</p> <p>night- (Texas dataset only) whether or not the location fell within daytime or nightime hours (y = nightime, n = daytime)</p> <p>droad - distance of location in meters from nearest road</p> <p>dwoody - distance of location in meters from nearest woody cover</p> <p>corn_prop250 - proportion of corn/sorghum landcover type within a 250m buffer around location</p> <p>cotton_prop500 - proportion of cotton/soybean landcover type within a 500m buffer around location</p> <p>herb_prop250 - proportion of herbaceous (i.e., grassy) landcover type within a 250m buffer around location</p> <p>rice_prop1000 - proportion of rice landcover type within a 1000m buffer around location</p> <p>sod_prop250 - proportion of sod landcover type within a 250m buffer around location</p> <p>presence - identifying used versus available locations (1 = used, as in, an actual GPS location of tracked bird; 0 = available, as in, a randomly generated location within the bird's area of use)</p> <p> </p> <p> </p> <p><strong>Tracking data </strong><em>bbsa_txStudy_movebank_20240731.csv</em></p> <p>Tracking data used for Texas study. Lotek PinPoint GPS data were imported and decoded via Movebank and relevant columns were selected. Columns are organized as follows:</p> <p> </p> <p>timestamp - time of GPS location in UTC (format = YYYY-MM-DD HH:MM:SS.SSS)</p> <p>location.long - longitude of GPS location in decimal degrees, projected in the WGS 84 datum</p> <p>location.lat - latitude of GPS location in decimal degrees, projected in the WGS 84 datum</p> <p>lotek.crc.status.text - value of Lotek's cyclical redundancy check (OK = good data, OK(corrected) = message was corrected with Lotek's error correction algorithm, Fail = data has been corrupted on transmission, and therefore cannot be trusted)</p> <p>sensor.type - type of sensor, all rows are "gps"</p> <p>tag.local.identifier - Argos ID of the attached Lotek PinPoint GPS transmitter</p> <p>individual.local.identifier - unique individual identifier, in this case the USGS-issued band number attached to each tracked bird</p> <p>individual.taxon.canonical.name - species taxonomical name, all rows are "Calidris subruficollis"</p> <p>sex - sex of tracked bird (M = male, F = female)</p> <p>age - age of tracke bird (AHY = after-hatch-year, HY = hatch-year)</p> <p> </p> <p> </p> <p><strong>Sod farm layer </strong><em>sodLayer_tgcp.tif</em><br>Raster layer representing sod farms in the Western Gulf Coastal Plain ecoregion of Texas. Resolution of 30m with values from 0 - 1, with 1 representing sod farms. Sod farms were identified via visual inspection, cross-referencing the U.S. Department of Agriculture's Cropland Data Layer with Google Earth high resolution imagery from 2022. This was a modification of preliminary data on sod farm distribution in the region compiled by B. Ortego, S. Gifford and J. Lyons.</p> <p> </p>
Table 1 in Survey of the Non-Migratory Birds of the Rock Islands Southern Lagoon World Heritage Site in Palau
<p>Table 1. List of the non-migratory wild birds detected during the survey of the Rock Islands Southern Lagoon World Heritage Site. * = Palau endemic species. <b>†</b> = Micronesia regional endemic species. ‡ = Introduced species. [EN] = Endangered species. Frequency values indicate the number of counts during which a species was detected. Shaded areas in the columns to the right indicate detection of a species at an island group: Column a=Ngeruktabel, b=Ulong, c=Mecherechar, d=Ngerechong, e=Ngemelis, f=Babelchomekang, g=Ulebsechel, h=Ngerukuid, i=Ngeanges, j=Kmekumer.</p><table><tbody><tr><th>English Name</th><th>Scientific Name</th><th>Frequency</th><th><b>a b c d e f g h i j</b></th></tr></tbody><tbody><tr><th>Palau Fruit Dove*</th><td><i>Ptilinopus pelewensis</i></td><td>129</td><td></td></tr><tr><th>Micronesian Starling <b>†</b></th><td><i>Aplonis opaca</i></td><td>122</td><td></td></tr><tr><th>Black Noddy</th><td><i>Anous minutus</i></td><td>114</td><td></td></tr><tr><th>Micronesian Myzomela <b>†</b></th><td><i>Myzomela rubratra</i></td><td>112</td><td></td></tr><tr><th>Collared Kingfisher</th><td><i>Todiramphus chloris</i></td><td>108</td><td></td></tr><tr><th>Palau Flycatcher*</th><td><i>Myiagra erythrops</i></td><td>103</td><td></td></tr><tr><th>Dusky White-eye*</th><td><i>Zosterops finschii</i></td><td>96</td><td></td></tr><tr><th>Palau Fantail*</th><td><i>Rhipidura lepida</i></td><td>93</td><td></td></tr><tr><th>Palau Bush Warbler*</th><td><i>Horornis annae</i></td><td>90</td><td></td></tr><tr><th>White Tern</th><td><i>Gygis alba</i></td><td>90</td><td></td></tr><tr><th>Micronesian Megapode <b>†</b> [EN]</th><td><i>Megapodius laperouse</i></td><td>84</td><td></td></tr><tr><th>Palau Swiftlet*</th><td><i>Aerodramus pelewensis</i></td><td>72</td><td></td></tr><tr><th>Morningbird*</th><td><i>Pachycephala tenebrosa</i></td><td>70</td><td></td></tr><tr><th>White-tailed Tropicbird</th><td><i>Phaethon lepturus</i></td><td>69</td><td></td></tr><tr><th>Micronesian Imperial Pigeon <b>†</b></th><td><i>Ducula oceanica</i></td><td>52</td><td></td></tr><tr><th>Sulphur-crested Cockatoo‡</th><td><i>Cacatua galerita</i></td><td>47</td><td></td></tr><tr><th>Palau Cicadabird*</th><td><i>Coracina monacha</i></td><td>28</td><td></td></tr><tr><th>Eclectus Parrot‡</th><td><i>Eclectus roratus</i></td><td>26</td><td></td></tr><tr><th>Nicobar Pigeon</th><td><i>Caloenas nicobarica</i></td><td>23</td><td></td></tr><tr><th><b>Giant White-eye*</b></th><td><i>Megazosterops palauensis</i></td><td>21</td><td></td></tr><tr><th>Citrine White-eye <b>†</b></th><td><i>Zosterops semperi</i></td><td>16</td><td></td></tr><tr><th>Blue-faced Parrotfinch</th><td><i>Erythrura trichoa</i></td><td>12</td><td></td></tr><tr><th>Palau Ground Dove*</th><td><i>Alopecoenas canifrons</i></td><td>11</td><td></td></tr><tr><th>Pacific Reef Heron</th><td><i>Egretta sacra</i></td><td>11</td><td></td></tr><tr><th>Brown Noddy</th><td><i>Anous stolidus</i></td><td>8</td><td></td></tr><tr><th>Bridled Tern</th><td><i>Onychoprion anaethetus</i></td><td>7</td><td></td></tr><tr><th>Rufous Night Heron</th><td><i>Nycticorax caledonicus</i></td><td>7</td><td></td></tr><tr><th>Rusty-capped Kingfisher*</th><td><i>Todiramphus pelewensis</i></td><td>6</td><td></td></tr><tr><th>Greater Crested Tern</th><td><i>Thalasseus bergii</i></td><td>5</td><td></td></tr><tr><th>Black-naped Tern</th><td><i>Sterna sumatrana</i></td><td>4</td><td></td></tr><tr><th>Little Pied Cormorant</th><td><i>Microcarbo melanoleucos</i></td><td>2</td><td></td></tr><tr><th>Tropical Shearwater</th><td><i>Puffinus bailoni</i></td><td>2</td><td></td></tr><tr><th>Great Frigatebird</th><td><i>Fregata minor</i></td><td>2</td><td></td></tr><tr><th>Brown Booby</th><td><i>Sula leucogaster</i></td><td>1</td><td></td></tr><tr><th>Slaty-legged Crake</th><td><i>Rallina eurizonoides</i></td><td>1</td><td></td></tr><tr><th>Palau Owl*</th><td><i>Pyrroglaux podargina</i></td><td>1</td><td></td></tr><tr><th>Palau Nightjar*</th><td><i>Caprimulgus phalaena</i></td><td>1</td><td></td></tr></tbody></table>
Extraordinarily rapid proliferation of cultured muscle satellite cells from migratory birds
<p>Migratory birds experience bouts of muscle growth and depletion as they prepare for, and undertake prolonged flight. Our studies of migratory bird muscle physiology <i>in vitro</i> led to the discovery that sanderling (<i>Calidris alba</i>) muscle satellite cells proliferate more rapidly than other normal cell lines. Here we determined the proliferation rate of muscle satellite cells isolated from five migratory species (sanderling; ruff, <i>Calidris pugnax</i>; western sandpiper, <i>Calidris mauri</i>; yellow-rumped warbler, <i>Setophaga coronata</i>; Swainson's thrush, <i>Catharus ustulatus</i>) from two families (shorebirds and songbirds) and with different migratory strategies. Ruff and sanderling satellite cells exhibited rapid proliferation, with population doubling times of 9.3±1.3 and 11.4±2.0 hrs whereas the remaining species' cell doubling times were ≥24 hrs. The results indicate that the rapid proliferation of satellite cells is not associated with total migration distance but may be related to flight bout duration and interact with lifespan.</p>
Fig. 2 in Swimming performance of the migratory Neotropical fish Leporinus reinhardti (Characiformes: Anostomidae)
Fig. 2. Number of tested fish per total length classes used to determine prolonged speed.
Data from: Hybridization alters growth and migratory life history expression of native trout
<p><span><span><span><span><span><span><span><span><span><span><span>Human-mediated hybridization threatens many native species, but the effects of introgressive hybridization on life history expression are rarely quantified, especially in vertebrates. We quantified the effects of non-native rainbow trout admixture on important life history traits including growth and partial migration behavior in three populations of westslope cutthroat trout over five years. Rainbow trout admixture was associated with increased summer growth rates in all populations, and decreased spring growth rates in two populations with cooler spring temperatures. These results indicate that non-native admixture may increase growth under warmer conditions, but cutthroat trout have higher growth rates during cooler periods. Non-native admixture consistently increased expression of migratory behavior, suggesting that there is a genomic basis for life history differences between these species. Our results show that effects of interspecific hybridization on fitness traits can be the product of genotype-by-environment interactions even when there are minor differences in environmental optima between hybridizing species. These results also indicate that while environmentally mediated traits like growth may play a role in population-level consequences of admixture, strong genetic influences on migratory life history differences between these species likely explains the continued spread of non-native hybridization at the landscape-level, despite selection against hybrids at the population-level.</span></span></span></span></span></span></span></span></span></span></span></p>
Changes in fat mass and migratory restlessness in northern wheatears
<p>To fuel their migratory endurance flights, most birds accumulate large quantities of fat prior to departure. It therefore seems logical that the decision to depart on a migratory flight depends on fuel stores, at least to some extent; very small fuel stores prohibit migratory endurance flight. However, studies linking migrants' estimated fuel stores (or sometimes actual fat stores) to departure likelihood have provided inconsistent results. Fuel stores are not static though, but highly dynamic. Instead of investigating fuel stores at a given time, we therefore focussed on quantifying the dynamic process of fuelling and how it affects migrants' motivation to depart. We did so by temporarily caging wild northern wheatears (<i>Oenanthe oenanthe</i>) at a stopover site during autumn migration. Birds were kept under controlled conditions, receiving ad libitum food. Changes in fat mass (measured by quantitative magnetic resonance) and migratory restlessness (an accurate proxy for migrants' motivation to depart from stopover) were closely monitored. We found that the change in fat mass was a good predictor of the change in migratory restlessness. Individuals accumulating little fat did not show much change in their restlessness, while individuals accumulating much fat showed a strong increase in restlessness. Our study therewith provides clear support for the idea that fat stores affect birds' motivation to migrate, and shows that for migrating birds, increasing fat stores are an important cue to depart from stopover. Fat accumulation affected the change in migratory restlessness in the same manner for relatively lean and relatively fat birds. Additionally, food intake or changes therein did not explain the change in migratory restlessness we observed. We therefore hypothesize that the fat tissue itself signals changes in energetic state, likely through production and release of hormones, affecting birds' motivation to migrate.</p>
Audio files and dataset: Guess who? Evaluating individual acoustic monitoring for males and females of the Tawny Pipit, a migratory passerine bird with a simple song
<p>We have uploaded the audio files (song files + recording files) and the dataset of the analysis conducted in our manuscript entitled ''Guess who? Evaluating individual acoustic monitoring for males and females of the<br> Tawny Pipit, a migratory passerine bird with a simple song''. This study is published in the scientific journal ''Journal of Ornithology''. We have also provided a txt. file, called 'Read me' explaining the uploaded data in more detail.</p>
Data for: Post-migratory nonbreeding movements of birds: a review and case study
<p><span>Seasonal migrations are fascinating and ecologically important, but many migratory species are declining as climate change and land-use change alter the habitats used by migrants across the annual cycle. While some migratory birds use a single wintering site, others undertake large-scale post-migratory movements during the nonbreeding season. Technological advances that enable tracking individual birds are uncovering more examples of post-migratory nonbreeding movements. Documenting these movements is important for conservation, which requires understanding when and where migrants use habitats throughout their range. Here, we reviewed existing literature and collected information on the post-migratory nonbreeding movements of 92 migratory bird species from 18 orders across six continents. Among these records, the most commonly reported drivers of movements were resource availability and climate. This strong dependence of post-migratory nonbreeding movements on birds' abiotic and biotic environments suggests that environmental change will impact the patterns of these movements and potentially the fitness of species that undertake them. We also reviewed post-migratory nonbreeding movements in North American-breeding thrushes from the genus Catharus to examine the drivers of these movements in five closely related migratory species. We find that species that are less territorial are more likely to use multiple sites during the nonbreeding season; however, there is little evidence for dietary, evolutionary, or environmental differences between thrush species that move during winter and those that are stationary. While we believe our study represents the most comprehensive list of species exhibiting post-migratory nonbreeding movements to date, biases in sampling, a lack of common terminology for these movements, and the still-nascent availability of inexpensive, lightweight tracking devices mean that there are probably many more populations that undertake such movements. Future research into the consequences of post-migratory nonbreeding movements for individual fitness and ecosystem services would advance our understanding of their conservation importance and their evolution.</span></p>
Data from: Sensitivity analysis of collision risk at wind turbines based on flight altitude of migratory waterbirds
<p>This dataset contains information on the distribution of geese and swans and the three-dimensional flight trajectories. The former was obtained through vehicle field surveys, interviews, and a literature review. The latter was obtained using ornithodolites.</p>
Seasonal ecosystem linkages contribute to the maintenance of migratory polymorphism in a salmonid population
<p><span>The influence of resource subsidies on animal </span><span>growth, survival, and reproduction </span><span>is well understood</span><span>, but their ultimate effects on life</span> <span>history</span> <span>have been </span><span>less</span><span> explored.</span> <span>Some wild species</span><span> have </span><span>a</span><span> partially migratory life</span> <span>history</span><span>, wherein migration is dictated based upon </span><span>threshold </span><span>traits</span><span> regulated </span><span>in part </span><span>by </span><span>the </span><span>seasonal </span><span>availability of resources. We conducted</span><span> a large-scale </span><span>field </span><span>manipulation</span><span> experiment</span><span> where</span><span> we </span><span>provided a</span><span> terrestrial invertebrate subsidy</span><span> to </span><span>red-spotted masu salmon</span><span>. Individuals in stream</span><span> reaches </span><span>that received a subsidy had, on average, a 53% increase in growth rate relative to those</span><span> in control reaches. </span><span>This increased</span><span> growth </span><span>resulted in a greater proportion of</span><span> individuals </span><span>reaching</span><span> the threshold body size </span><span>and smolting</span><span> in </span><span>the </span><span>autumn. Consequently, 19</span><span>–</span><span>55% of females </span><span>in subsidized</span><span> reaches</span><span> became migratory</span><span>, whereas 0</span><span>–</span><span>14% </span><span>became migratory </span><span>in the control reaches. Our findings highlight seasonal ecosystem linkage </span><span>as</span><span> a key ecosystem </span><span>property for maintaining</span><span> migratory polymorphism in partially</span> <span>migratory animals</span><span>.</span></p>
Low conversion efficiency of flight power increases with speed in the migratory bat Nathusius' pipistrelle
<p>The efficiency with which flying animals convert metabolic fuel to mechanical power dictates an individual's flight behaviour and energy requirements. Despite the significance of this parameter, we lack empirical data on conversion efficiency for most species. Furthermore, the assumption that efficiency is constant across flight speeds has yet to be empirically confirmed, revealing a major outstanding question in animal flight. We show, through direct measurements of metabolic and aerodynamic power, that conversion efficiency in the migratory bat (<em>Pipistrellus nathusii</em>) increases with flight speed between 7.0 and 10.4%. Our findings suggest that peak conversion efficiency in this species occurs near maximum range speed, where the cost of transport is minimized. A meta-analysis of 16 bird and 8 bat species revealed a strong scaling relationship between estimated conversion efficiency and body mass, with no discernible differences between bats and birds. This has profound consequences for modelling flight behaviour as estimates assuming 23% efficiency underestimate flight costs for <em>P. nathusii</em> by almost 50% on average (36-62%). Our findings suggest that conversion efficiency may vary around an ecologically relevant optimum speed and provides a crucial baseline for investigating how this may drive the variation in conversion efficiency between species.</p>
Neuronal activation in the geomagnetic responsive region Cluster N covaries with nocturnal migratory restlessness in white-throated sparrows
<p>Cluster N is a region of the visual forebrain of nocturnally migrating songbirds that supports the geomagnetic compass of nocturnal migrants. Cluster N expresses immediate-early genes (ZENK), indicating neuronal activation. This neuronal activity has only been recorded at night during the migratory season. Night-to-night variation in Cluster N activity in relation to migratory behaviour has not been previously examined. We tested whether Cluster N is activated only when birds are motivated to migrate and presumably engage their magnetic compass. We measured immediate-early gene activation in Cluster N of white-throated sparrows (<em>Zonotrichia</em> <em>albicollis</em>) in three conditions: daytime, nighttime migratory restless, and nighttime resting. Birds in the nighttime migratory restlessness group had significantly greater numbers of ZENK-labelled cells in Cluster N compared to both the daytime and the nighttime resting groups. Additionally, the degree of migratory restlessness was positively correlated with the number of ZENK-labelled cells in the nighttime migratory restless group. Our study adds to the number of species observed to have neural activation in Cluster N and demonstrates for the first time that immediate early gene activation in Cluster N is correlated with the amount of active migratory behaviour displayed across sampled individuals. We conclude that Cluster N is facultatively regulated by the motivation to migrate, together with nocturnal activity, rather than obligatorily active during the migration season.</p>
Survival and cause-specific mortality in adult females of a northern migratory ungulate
<p>Survival of ungulates can vary seasonally due to changing environmental conditions, e.g., weather or predation pressure. The wild forest reindeer (<em>Rangifer tarandus fennicus</em>, 'WFR') migrate between calving and wintering grounds. The annual cycle of female WFR includes four main seasons (wintering, calving, rutting and autumn migration) during which they are subject to different conditions, but almost nothing is known about their survival or mortality patterns. We measured survival in 305 GPS-tagged female WFR in two subpopulations in Finland (2010–2022). Data were analysed for inter-annual and seasonal patterns in survival and cause-specific mortality (predation, traffic, accidents and unknown causes) with known-fate models. Inter-annual survival in Suomenselkä (mean 0.90) showed an increasing trend during the study and was higher than in Kainuu (mean 0.84) which showed a declining trend. Seasonal variation in survival was population dependent. Survival was lowest in Kainuu during the winter while in Suomenselkä, it was lowest during autumn migration. Concerning cause-specific mortality, seasonal variation mirrored variation in predation that was the primary cause of mortality (55% of cases). Wolves (<em>Canis lupus</em>) killed the majority of predated individuals (58%). Predation rates were two times higher in Kainuu where the wolf density was higher. We provide the first adult survival estimates for WFR and show that the seasonal variation in survival is clearly different between these two northern ungulate populations. This indicates that seasonal variation in survival is determined by site-dependent factors and cannot be generalised across populations.<span class="x_ContentPasted0"> </span></p>
raw characterization data for the article "Synthesis of Propargyl Silanes from Terminal Alkynes via a Migratory Sonogashira Reaction"
<p>Raw NMR, IR and MS data for the article "Synthesis of Propargyl Silanes from Terminal Alkynes via a Mi-gratory Sonogashira Reaction" published in Chemical Communications, DOI: </p> <p><a href="https://doi.org/10.1039/D3CC01847D">https://doi.org/10.1039/D3CC01847D</a></p> <p>The number of the folders either correspond to compounds numbers in the article or the name of the folder is self-describing. All details concerning conditions and equipment for measurements can be found in the supporting information of the article.</p>
Data from: Conserving habitat for migratory ungulates: how wide is a migration corridor?
<ol> <li>Conserving migratory ungulates relies on the analysis of GPS collar data and associated maps of migration corridors to inform management and policy actions. Current methods for identifying migratory corridors use complex statistical models designed to account for movement uncertainty rather than estimating the amount of space required by animals to migrate. Further, such methods can complicate conservation efforts by producing highly variable corridor widths and non-contiguous corridors that do not fully connect seasonal ranges.</li> <li>To remedy this, we propose an intuitive line buffer approach for delineating individual migration corridors that is simple to implement and focuses on the functional corridor widths needed by migratory ungulates. </li> <li>By buffering a line that connects successive GPS locations, we can delineate individual migration corridors with consistent widths that are robust to variable parameters (GPS fix-rate, travel speed, tortuosity) and provide contiguous connection between seasonal ranges. Using a combination of expert knowledge, simulation, and 10-min GPS collar data collected from mule deer (<em>Odocoileus</em> <em>hemionus</em>) and pronghorn (<em>Antilocapra</em> <em>americana</em>), we suggest 400 to 600 m are reasonable estimates of functional migration corridor widths for individuals of those species.</li> <li>Our line buffer approach is intended to simplify migration corridor delineation, improve transparency, and encourage a broader discussion of functional corridor widths. These considerations help advance efforts to conserve habitat within migration corridors and prioritize conservation efforts within a single corridor or across multiple corridors.</li> </ol>
Reproductive phenology is a repeatable, heritable trait linked to the timing of other life history events in a migratory marine predator
<p>Population-level shifts in reproductive phenology in response to environmental change are common, but whether individual-level responses are modified by demographic and genetic factors remains less well understood. We used mixed models to quantify how reproductive timing varied across 1,755 female southern elephant seals (<em>Mirounga</em> <em>leonina</em>) breeding at Marion Island in the Southern Ocean (1989–2019) and to identify the factors that correlate with phenological shifts within- and between individuals. We found strong support for covariation in the timing of breeding arrival dates and the timing of the preceding moult. Breeding arrival dates were more repeatable at the individual-level, as compared to the population-level, even after accounting for individual traits (wean date as a pup, age and breeding experience) associated with phenological variability. Mother-daughter similarities in breeding phenology were also evident, indicating that additive genetic effects may contribute to between-individual variation in breeding phenology. Over 30 years, elephant seal phenology did not change towards earlier or later dates, and we found no correlation between annual fluctuations in phenology and indices of environmental variation. Our results show how maternal genetic (or non-genetic) effects, individual traits and linkages between cyclical life-history events can drive within- and between-individual variation in reproductive phenology.</p>
Dataset for: Utilizing high-resolution genetic markers to track population-level exposure of migratory birds to renewable energy development
<p class="MsoNormal"><span>With new motivation to increase the proportion of energy demands met by zero-carbon sources, there is a greater focus on efforts to assess and mitigate the impacts of renewable energy development on sensitive ecosystems and wildlife, of which birds are of particular interest. One challenge for researchers, due in part to a lack of appropriate tools, has been estimating the effects from such development on individual breeding populations of migratory birds. To help address this, we utilize a newly developed, high-resolution genetic tagging method to rapidly identify the breeding population of origin of carcasses recovered from renewable energy facilities and combine them with maps of genetic variation across geographic space (called 'genoscapes') for five species of migratory birds known to be exposed to energy development, to assess the extent of population-level effects on migratory birds. We demonstrate that most avian remains collected were from the largest populations of a given species. In contrast, those remains from smaller, declining populations made up a smaller percentage of the total number of birds assayed. Results suggest that application of this genetic tagging method can successfully define population-level exposure to renewable energy development and may be a powerful tool to inform future siting and mitigation activities associated with renewable energy programs.</span></p>
Low-coverage whole genome sequencing for highly accurate population assignment: Mapping migratory connectivity in the American Redstart (Setophaga ruticilla)
<p>Understanding the geographic linkages among populations across the annual cycle is an essential component for understanding the ecology and evolution of migratory species and for facilitating their effective conservation. While genetic markers have been widely applied to describe migratory connections, the rapid development of new sequencing methods, such as low-coverage whole genome sequencing (lcWGS), provides new opportunities for improved estimates of migratory connectivity. Here, we use lcWGS to identify fine-scale population structure in a widespread songbird, the American Redstart (<em>Setophaga</em> <em>ruticilla</em>), and accurately assign individuals to genetically distinct breeding populations. Assignment of individuals from the nonbreeding range reveals population-specific patterns of varying migratory connectivity. By combining migratory connectivity results with demographic analysis of population abundance and trends, we consider full annual cycle conservation strategies for preserving numbers of individuals and genetic diversity. Notably, we highlight the importance of the Northern Temperate-Greater Antilles migratory population as containing the largest proportion of individuals in the species. Finally, we highlight valuable considerations for other population assignment studies aimed at using lcWGS. Our results have broad implications for improving our understanding of the ecology and evolution of migratory species through conservation genomics approaches.</p>
Data from: Arriving late and lean at a stopover site is selected against in a declining migratory bird population
<p>Loss and/or deterioration of refuelling habitats have caused population declines in many migratory bird species, but whether this results from unequal mortality among individuals varying in migration traits remains to be shown. Based on 13 years of body mass and size data of great knots (<em>Calidris tenuirostris</em>) at a stopover site of the Yellow Sea, combined with resightings of individuals marked at this stopover site along the East Asian-Australasian Flyway, we assessed year to year changes in annual apparent survival rates, and how apparent survival differed between migration phenotypes (their i.e. migration timing and fuel stores). The measurements occurred over a period of habitat loss and/or deterioration in this flyway. We found that the annual apparent survival rates of great knots rapidly declined from 2006 to 2018, late-arriving individuals with small fuel stores exhibiting the lowest apparent survival rate. There was an advancement in mean arrival date and an increase in the mean fuel load of stopping birds over the study period. Our results suggest that late-arriving individuals with small fuel loads were selected against. Thus, habitat loss and/or deterioration at staging sites may cause changes in the composition of migratory phenotypes at the population-level.</p>
A gradual migratory divide determines not only the direction of migration but also migration strategy of a social migrant bird
<p>Migratory divides separate populations of migratory animals, facilitating the evolution of intraspecific differences in migration strategies. Migration strategies are expected to be different for birds using different flyways and environments, but the knowledge regarding the impact of the flyway on individual migration strategies is scarce. By using satellite tracking and neckband resightings, we reveal the existence and structure of a gradual migratory divide between two European flyway populations of greylag geese <em>Anser anser</em>. Birds breeding at the far end of the Gulf of Bothnia in the Baltic Sea coast use the Western Flyway; those breeding in the Gulf of Finland the Central Flyway and those breeding between these extremes scatter to the two flyways. By using Gaussian process modelling, we show that migration strategies differed between the flyways. The birds using Western Flyway migrated earlier in autumn, performed longer annual migration and made a clear stopover during migration, whereas the birds using Central Flyway flew directly to their wintering sites. The gradual migratory divide that also divides migration strategies provides insights to migratory divides in birds with learned migration. Distinct migration strategies in different flyways provide exciting possibilities to further study the factors driving migration strategies.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.