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36 results for “range connectivity”
Jornada Basin and Experimental Range Mesquite Herbicide Project (JERHM) Connectivity Modifier Data, 2023
This dataset includes plant community composition, plant litter, soil depth, and shrub interspace fetch distance data collected in 2023 as part of the Jornada Experimental Range Herbicide Mesquite Project (JERHM). Data were collected to characterize plant community and ecosystem resource (plant litter, soil depth) responses to the use of Connectivity Modifiers (ConMods), which are used to reduce bare ground connectivity, alongside herbicide application to reduce honey mesquite (Neltuma glandulosa [=Prosopis glandulosa]) encroachment. ConMod and control (rebar-only) arrays were installed on 16 paired, 5-hectare plots, with one plot within each pair randomly selected to receive herbicide treatment in 2021 to reduce N. glandulosa abundance, or left untreated for comparison. Approximately 6 months after herbicide application (spring 2022), 12-unit ConMod and control (rebar-only) arrays were installed on experimental plots within 8 randomly selected N. glandulosa shrub interspaces (n = 8 arrays per plot, 4 per array type). Data were collected in the fall of 2023 at the end of the second growing season following array installation. There are no immediate plans to continue data collection.
Data from: Integrating tracking and resight data enables unbiased inferences about migratory connectivity and winter range survival from archival tags
<p>Archival geolocators have transformed the study of small, migratory organisms but analysis of data from these devices requires bias correction because tags are only recovered from individuals that survive and are re-captured at their tagging location. Data and code provided in this repository can be used to replicate the simulation and Painted Bunting case study results presented by Rushing et al. (2021) showing that integrating geolocator recovery data and mark–resight data enables unbiased estimates of both migratory connectivity between breeding and nonbreeding populations and region-specific survival probabilities for wintering locations.</p>
Figure3. APCs A1-A4 connected within range of cohesion-factor-threshold form members of one ARB-AIDEN: A Density Conscious Artificial Immune System for Automatic Discovery of Arbitrary Shape Clusters in Spatial Patterns
<p>Figure3 depicts this process. The<br> model with the above specification then effectively detects self or non-self pathogens. In terms of<br> its application to the task of clustering, this interpretation means making the affinities high within<br> clusters and low across clusters. A pathogen corresponding to an outlier would not stimulate a TCR<br> sufficiently and may not form part of any ARB.</p>
Fig. 2 in Exploring potential range connectivity of sun bear (Carnivora: Ursidae: Ursinae)
Fig. 2. Frequency plots of land type values from the Terrestrial Ecosystem Environment Observation by Satellites (TREES; Stigbig et al., 2003). TREES land type values were calculated to 20,000 random points generated equally between non-habitat, marginal, sub-optimal, and core habitat. Land type was reclassified into 12 categories; 1–8 are categories in which bear use has been previously detected (1–3 = evergreen; 4 = deciduous, woodland; 5 = mangrove; 6 = swamp, woodland; 7–8 = mosaic of woodland, secondary, evergreen and cropland), and 9–12 are considered non-habitat (9 = cropland, shrub; 10 = cropland, bare land; 11 = rock, limestone; 12 = water). To correct for errors on the TREES map due to deforestation since 2000, % tree cover in 2014 was extracted for each random point, and points with no tree cover in 2014 reclassified as falling in non-viable habitat. TREES land classification values within non-habitat were more often classified as areas considered as non-viable bear habitat (i.e., cropland, shrub, bare land, rock). Second to non-habitat, marginal habitat had the highest proportion of points within non-viable bear habitat categories. In sub-optimal and core habitat, land classification tended to be areas of potential bear habitat (i.e., evergreen, deciduous forest, and other forms of mosaic forest).
Fig. 1 in Exploring potential range connectivity of sun bear (Carnivora: Ursidae: Ursinae)
Fig. 1. Density plots of Human Influence Index values within areas classified as habitat and non-habitat within sun bear range. Human Influence Index values (Sanderson et al., 2002) were calculated to 30,000 random points generated equally within areas of non-habitat and habitat. Human Influence Index values were on average 13.8 points higher in areas classified as non-habitat (t = –95.2, df = 29658, p <0.001, x – within non-habitat = 36.7, SD = 13.1, x – within habitat = 23, SD = 11.8) supporting our assumption that habitat is different from non-habitat.
Fig. 3 in Exploring potential range connectivity of sun bear (Carnivora: Ursidae: Ursinae)
Fig. 3. Sun bear landscape fragmentation and connectivity in Southeast Asia, India and Bangladesh. A) Core and sub-optimal contiguous range is assumed to positively impact bear movement (i.e., connectivity), although dependent on associated levels of human influence and roads. Visual analysis identified seven potential subpopulations of sun bears; i) northern Mainland, ii) Central Myanmar, iii) Central SE Asia, iv) South-central SE Asia, v) Thai-Malay peninsula, vi) Sumatra, vii) Borneo (divided by dashed lines). Within these potential subpopulations there were many 'At Risk' areas where sun bears may be vulnerable to becoming isolated due to potential barriers to movement including habitat fragmentation, high human influence and roads (identified by red ovals and numbers 1–16 correspond with the IDs listed in Table 2). B) High Human Influence and road network are assumed to be significant barriers to bear movement across the sun bear landscape.
Non-reproductive dispersal: An important driver of migratory range dynamics and connectivity
<p>Dispersal is the primary ecological process underpinning spatial dynamics in motile species by generating flux in reproductive locations over time. In migratory species, dispersal can also occur around non-breeding ranges, but this form currently lacks a unifying theoretical framework. We present a novel conceptual model for dispersal in migrants that builds upon existing literature, differentiating 'reproductive' dispersal (i.e. changes in breeding locations) from 'non-reproductive' dispersal, which we define as movements resulting in inter-annual or inter-generational changes in non-breeding locations. Crucially, unlike reproductive dispersal where movement outcomes are naturally propagated between generations, the outcomes of non-reproductive dispersal can be non-heritable even if dispersers survive to reproduce. We simulate a non-social migratory population with a genetically-determined migratory programme to model how heritability of this program influences both migratory connectivity and range shift propensity. When exposed to spatially-uncoupled shifts in habitable ranges (i.e. seasonal climate niches shifting at different rates), long-term persistence of simulated populations required changes in migratory programmes to arise through heritable forms of non-reproductive dispersal (e.g. mutations in migratory gene complexes). By contrast, non-heritable dispersal mechanisms (e.g. weather drift, navigation errors) did not drive long-term shifts in non-breeding ranges, despite being a major component of realised dispersal and migratory connectivity patterns. Migratory connectivity metrics conflate these heritable and non-heritable drivers of non-reproductive dispersal and therefore have limited power in predicting spatial population responses to environmental change. Our models provide a framework for improving our understanding of spatial dynamics in migratory populations and highlight the importance of teasing apart the genetic or cultural mechanisms that drive inter-generational migratory variability in order to evaluate and predict range plasticity in migrants.</p>
Non-reproductive dispersal: An important driver of migratory range dynamics and connectivity
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Data from: Integrating tracking and resight data enables unbiased inferences about migratory connectivity and winter range survival from archival tags
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Data from: Functional connectivity and home range inferred at a microgeographic landscape genetics scale in a desert-dwelling rodent
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Identifying the environmental drivers of corridors and predicting connectivity between seasonal ranges in multiple populations of Alpine ibex (Capra ibex) as tools for conserving migration
<p># GPS locations of Alpine ibex</p> <p>This dataset contains migratory tracks of Alpine ibex identified using the application Migration Mapper (https://migrationinitiative.org/content/migration-mapper) and used in the work <strong>Identifying the environmental drivers of corridors and predicting connectivity between seasonal ranges in multiple populations of Alpine ibex (<em>Capra ibex</em>) as tools for conserving migration</strong></p> <p># Dataset structure</p> <p>Each row of the dataset represents a GPS location with its coordinates contained in the x (longitude) and y(latitude) columns. Coordinates are given in wgs84 (epsg 4326).<br> The column t1_ informs on the date and time the location was recorded.<br> The id and pop columns provide information about the identity of the animal and the population to which it belongs.</p> <p> </p>
Pronghorn population genomics show connectivity at the core of their range
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Importance of spatio-temporal connectivity to maintain species experiencing range shifts
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Data from: Mechanistic home range capture–recapture models for the estimation of population density and landscape connectivity
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Data from: Predicting global population connectivity and targeting conservation action for snow leopard across its range
Movements of individuals within and among populations help to maintain genetic variability and population viability. Therefore, understanding landscape connectivity is vital for effective species conservation. The snow leopard is endemic to mountainous areas of Central Asia and occurs within 12 countries. We assess potential connectivity across the species' range to highlight corridors for dispersal and genetic flow between populations, prioritizing research and conservation action for this wide-ranging, endangered top-predator. We used resistant kernel modeling to assess snow leopard population connectivity across its global range. We developed an expert-based resistance surface that predicted cost of movement as functions of topographical complexity and land cover. The distribution of individuals was simulated as a uniform density of points throughout the currently accepted global range. We modeled population connectivity from these source points across the resistance surface using three different dispersal scenarios that likely bracket the lifetime movements of individual snow leopard: 100km, 500km and 1000km. The resistant kernel models produced predictive surfaces of dispersal frequency across the snow leopard range for each distance scenario. We evaluated the pattern of connectivity in each of these scenarios and identified potentially important movement corridors and areas where connectivity might be impeded. The models predicted two regional populations, in the north and south of the species range respectively, and revealed a number of potentially important connecting areas. Discrepancies between model outputs and observations highlight unsurveyed areas of connected habitat that urgently require surveying to improve understanding of the global distribution and ecology of snow leopard, and target land management actions to prevent population isolation. The connectivity maps provide a strong basis for directed research and conservation action, and usefully direct the attention of policy makers.
Data from: Landscape resistance and habitat combine to provide an optimal model of genetic structure and connectivity at the range margin of a small mammal
We evaluated the effect of habitat and landscape characteristics on the population genetic structure of the white-footed mouse. We develop a new approach that uses numerical optimization to define a model that combines site differences and landscape resistance to explain the genetic differentiation between mouse populations inhabiting forest patches in southern Québec. We used ecological distance computed from resistance surfaces with Circuitscape to infer the effect of the landscape matrix on gene flow. We calculated site differences using a site index of habitat characteristics. A model that combined site differences and resistance distances explained a high proportion of the variance in genetic differentiation and outperformed models that used geographical distance alone. Urban and agriculture related land uses were, respectively, the most and the least resistant landscape features influencing gene flow. Our method detected the effect of rivers and highways as highly resistant linear barriers. The density of grass and shrubs on the ground best explained the variation in the site index of habitat characteristics. Our model indicates that movement of white-footed mouse in this region is constrained along routes of low resistance. Our approach can generate models that may improve predictions of future northward range expansion of this small mammal.
Data from: Unusually limited pollen dispersal and connectivity of Pedunculate oak (Quercus robur) refugial populations at the species' southern range margin
Low-latitudinal range margins of temperate and boreal plant species typically consist of scattered populations that persist locally in microrefugia. It remains poorly understood how their refugial habitats affect patterns of gene flow and connectivity, key components for their long-term viability and evolution. We examine landscape-scale patterns of historical and contemporary gene flow in refugial populations of the widespread European forest tree Pedunculate oak (Quercus robur) at the species' southwestern range margin. We sampled all adult trees (n = 135) growing in a 20 km long valley and genotyped 724 acorns from 72 mother trees at 17 microsatellite loci. The ten oak stands that we identified were highly differentiated and formed four distinct genetic clusters, despite sporadic historical dispersal being detectable. By far most contemporary pollination occurred within stands, either between local mates (85.6%) or through selfing (6.8%). Pollen exchange between stands (2.6%) was remarkably rare given their relative proximity and was complemented by long-distance pollen immigration (4.4%) and hybridization with the locally abundant Quercus pyrenaica (0.6%). The frequency of between-stand mating events decreased with increasing size and spatial isolation of stands. Overall, our results reveal outstandingly little long-distance gene flow for a wind-pollinated tree species. We argue that the distinct landscape characteristics of oaks' refugial habitats, with a combination of a rugged topography, dense vegetation and humid microclimate, are likely to increase plant survival but to hamper effective long-distance pollen dispersal. Moreover, local mating might be favoured by high tree compatibility resulting from genetic purging in these long-term relict populations.
Data from: Movement is the glue connecting home ranges and habitat selection
1. Animal space use has been studied by focusing either on geographic (e.g. home ranges, species' distribution) or on environmental (e.g. habitat use and selection) space. However, all patterns of space use emerge from individual movements, which are the primary means by which animals change their environment. 2. Individuals increase their use of a given area by adjusting two key movement components: the duration of their visit and/or the frequency of revisits. Thus, in spatially heterogeneous environments, animals exploit known, high-quality resource areas by increasing their residence time (RT) in and/or decreasing their time to return (TtoR) to these areas. We expected that spatial variation in these two movement properties should lead to observed patterns of space use in both geographic and environmental spaces. We derived a set of nine predictions linking spatial distribution of movement properties to emerging space-use patterns. We predicted that, at a given scale, high variation in RT and TtoR among habitats leads to strong habitat selection and that long RT and short TtoR result in a small home range size. 3. We tested these predictions using moose (Alces alces) GPS tracking data. We first modelled the relationship between landscape characteristics and movement properties. Then, we investigated how the spatial distribution of predicted movement properties (i.e. spatial autocorrelation, mean, and variance of RT and TtoR) influences home range size and hierarchical habitat selection. 4. In landscapes with high spatial autocorrelation of RT and TtoR, a high variation in both RT and TtoR occurred in home ranges. As expected, home range location was highly selective in such landscapes (i.e. second-order habitat selection); RT was higher and TtoR lower within the selected home range than outside, and moose home ranges were small. Within home ranges, a higher variation in both RT and TtoR was associated with higher selectivity among habitat types (i.e. third-order habitat selection). 5. Our findings show how patterns of geographic and environmental space use correspond to the two sides of a coin, linked by movement responses of individuals to environmental heterogeneity. By demonstrating the potential to assess the consequences of altering RT or TtoR (e.g. through human disturbance or climatic changes) on home range size and habitat selection, our work sets the basis for new theoretical and methodological advances in movement ecology
Distribution. Disjunct range, recorded in NE India and Bangladesh (Chittagong) and in SE Asia (Thailand, Laos, Vietnam, and Cambodia). Presence in all Myanmar expected, which would connect these areas. in Pteropodidae
Distribution. Disjunct range, recorded in NE India and Bangladesh (Chittagong) and in SE Asia (Thailand, Laos, Vietnam, and Cambodia). Presence in all Myanmar expected, which would connect these areas.
Knee Connect: Measuring Range of Motion and Gait Metrics After Total Knee Arthroplasty
ClinicalTrials.gov study NCT03651739. IPD Sharing: UNDECIDED. Countries: 1. Publications: 7.
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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)
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DANDI Archive for NWB datasets
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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.