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207 results for “spatial response”
Data from: Integrating over uncertainty in spatial scale of response within multispecies occupancy models yields more accurate assessments of community composition
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Data from: Multiple scales of spatial heterogeneity control soil respiration responses to precipitation across a dryland rainfall gradient
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Data from: Drivers of the spatial scale that best predict primate responses to landscape structure
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Data from: Expanded consumer niche widths may signal an early response to spatial protection
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Data from: Fear on the move: predator hunting mode predicts variation in prey mortality and plasticity in prey spatial response
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Predicting changes in molluscan spatial distributions in mangrove forests in response to sea-level rise
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Transcriptional acclimation and spatial differentiation characterize drought response by the ectomycorrhizal fungus Suillus pungens
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Data from: Personalities influence spatial responses to environmental fluctuations in wild fish
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Data from: The traits that predict the magnitude and spatial scale of forest bird responses to urbanization intensity
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Supplementary materials for "Spatial runoff updating based on hydrologic system differential response for flood forecasting"
<p>Supplementary material 1: Observations in the BSK basin including precipitation, evaporation and discharge. </p> <p>Supplementary material 2: Observations in the SW basin including precipitation, evaporation and discharge. </p> <p>Supplementary material 3: Results for floods 20071006 in the BSK basin</p> <p>Supplementary material 4: Results for floods 19890526 in the SW basin</p> <p>Supplementary material 5: Results with different lead time in the BSK and SW basins</p>
Response of flow and saltating particle characteristics to bed roughness and particle spatial density
<p>The data were used in the paper "Response of flow and saltating particle characteristics to bed roughness and particle spatial density" which was submitted to "Journal of Geophysical Research-Earth Surface". In this paper, the effects of bed roughness and particle spatial density on the bedload transport are investigated by numerical simulations. The distributions of key parameters for saltation, including their changes, are assessed using the PDF curves. It is found that bed roughness is likely composed of particles of equivalent size for fine particles.</p>
Data from: Sociodemographic factors modulate the spatial response of brown bears to vacancies created by hunting
1.There is a growing recognition of the importance of indirect effects from hunting on wildlife populations, e.g., social and behavioral changes due to harvest, which occur after the initial offtake. Nonetheless, little is known about how the removal of members of a population influences the spatial configuration of the survivors. 2.We studied how surviving brown bears (Ursus arctos) used former home ranges that had belonged to casualties of the annual bear hunting season in southcentral Sweden (2007-2015). We used resource selection functions to explore the effects of the casualty's and survivor's sex, age, and their pairwise genetic relatedness, population density, and hunting intensity on survivors' spatial responses to vacated home ranges. 3.We tested the competitive release hypothesis, whereby survivors that increase their use of a killed bear's home range are presumed to have been released from intraspecific competition. We found strong support for this hypothesis, as survivors of the same sex as the casualty consistently increased their use of its vacant home range. Patterns were less pronounced or absent when the survivor and casualty were of opposite sex. 4.Genetic relatedness between the survivor and the casualty emerged as the most important factor explaining increased use of vacated male home ranges by males, with a stronger response from survivors of lower relatedness. Relatedness was also important for females, but it did not influence use following removal; female survivors used home ranges of higher related female casualties more, both before and after death. Spatial responses by survivors were further influenced by bear age, population density, and hunting intensity. 5.We have showed that survivors exhibit a spatial response to vacated home ranges caused by hunting casualties, even in non-territorial species such as the brown bear. This spatial reorganization can have unintended consequences for population dynamics and interfere with management goals. Altogether, our results underscore the need to better understand the short- and long-term indirect effects of hunting on animal social structure and their resulting distribution in space.
Mismatches between birds' spatial and temporal dynamics reflect their delayed response to global changes
<p>Global changes alter the dynamics of biodiversity, and are forecasted to continue or worsen in the decades to come. Modelling approaches used to anticipate these impacts are mainly based on the equivalence between spatial and temporal response to environmental forcings, generally called space-for-time substitution. However, several processes are known to generate deviations between spatial and temporal responses, potentially undermining the prediction based on space-for-time substitution.</p> <p>We here used high-resolution data from the french breeding bird survey to quantify and map the deviation between spatial and temporal patterns of bird abundances resulting from the dynamics of 124 species monitored in 2133 sites between 2001 and 2012. Using independent empirical data, we then tested specific predictions linked to the determinants (anthropogenic activities) and processes (lagged responses to environmental changes) potentially generating these deviations. We found that deviations between spatial and temporal patterns of abundances were particularly structured in space for bird communities. Following our predictions, these space–time deviations were positively correlated with the human influence on ecosystems, and linked with colonization–extinction ratios and community completeness, two markers of ongoing delayed responses to environmental changes. Our results suggest that the deviations between spatial and temporal patterns are related to recent anthropogenic environmental changes and disequilibrium responses to these changes. Investigating deviations between spatial and temporal patterns of biodiversity might open promising perspectives for a formal quantification of disequilibrium state of biodiversity at large spatial scale.</p>
Data from: Dynamics of bird assemblages in response to temporally and spatially variable resources in arid Australia
<p>Bird assemblages in arid Australia are often characterised as being highly variable through time in response to boom and bust dynamics, although the importance of habitat in structuring assemblages at a local scale is also recognised. We use a novel approach to investigate the importance of rainfall variability in structuring bird assemblages in a resource-limited environment. Monthly bird surveys were conducted at ten plots for eight years at a botanical and zoological park in central Australia, including five irrigated plots within a fenced area and five natural plots outside. Irrigation – used to promote growth, flowering, and fruiting of plants – created an artificial resource-enhanced environment against which the response of birds to natural fluctuations in season and rainfall were compared. Species richness was generally maintained at a higher level in resource-enhanced plots during dry times but was higher in natural sites when rainfall was high, mainly due to increases in granivores and insectivores. Honeyeaters were consistently more abundant at irrigated sites. Rainfall was important in structuring bird assemblages at all plots; however, assemblages were more stable in irrigated plots and did not respond as dramatically to a period of very high rainfall. The comparative smoothing of fluctuations in the composition and abundance of birds in irrigated areas highlights the importance of primary productivity, normally tied to rainfall, in driving temporal change in arid-zone bird communities. There was also evidence that different plots in differing habitats supported distinct bird assemblages and that this spatial distinctiveness persisted irrespective of rainfall and determined, to some extent, the response to rainfall. Our study is one of few long-term studies of arid bird assemblages and highlights the importance of both long-term cycles of productivity driven by rain and season as well as site differences in the dynamics of arid zone bird communities. These insights are particularly valuable as climate change further exacerbates rainfall variability worldwide and initiatives to conserve avifauna in increasingly extreme environments may be required.</p>
Age-specific habitat preference, carrying capacity, and landscape structure determine the response of population spatial variability to fishing-driven age truncation
<p>1. Understanding the mechanisms underlying spatial variability of exploited fish is critical for the sustainable management of fish stocks. Empirical studies suggest that size-selective fishing can elevate fish population spatial variability (i.e., more heterogeneous distribution) through age truncation, making the population less resilient to changing environment. However, species differ in how their spatial variability respond to age truncation and the underlying mechanisms remain unclear.</p> <p>2. We hypothesize that age-specific habitat preference, together with environmental carrying capacity and landscape structure, determines the response of population spatial variability to fishing-induced age truncation. To test these hypotheses, we design an individual-based model of an age-structured fish population on a two-dimensional landscape under size-selective fishing. Individual fish reproduces and survives, and moves between habitats according to age-specific habitat preference and density-dependent habitat selection.</p> <p>3. Population spatial variability elevates with increasing age truncation and the response is stronger for populations with stronger age-specific habitat preference. On a gradient landscape, reducing carrying capacity elevates the relative importance of density-dependence in habitat selection, which weakens the response of spatial variability to age truncation for populations with strong age-specific habitat preference. On a fragmented landscape, both populations with strong and weak age-specific habitat preferences are restricted at local optimal habitats, and reducing carrying capacity weakens the responses of spatial variability to age truncation for both populations.</p> <p>4. Synthesis and applications. We demonstrate that to track and predict the changes in population spatial variability under exploitation, it is essential to consider the interactive effects of age-specific habitat preference, carrying capacity, and landscape structure. To improve spatial management in fisheries, it is crucial to enhance empirical and theoretical developments in the methodology to quantify age-specific habitat preference of marine fish, and to understand how climatic change influences carrying capacity and landscape continuity.</p>
Supplementary material 1 from: Zhao M, Tian Y, Dong N, Hu Y, Tian G, Lei Y (2022) Spatial and temporal dynamics of habitat quality in response to socioeconomic and landscape patterns in the context of urbanization: A case in Zhengzhou City, China. Nature Conservation 48: 185-212. https://doi.org/10.3897/natureconservation.48.85179
Notes on the data
Camera traps Red deer exhibit spatial and temporal responses to hiking activity
<p>Outdoor recreation has the potential to impact the spatial and temporal distribution of animals. We explore interactions between red deer (Cervus elaphus) and hikers along a popular hiking path in the Scottish Highlands. We placed camera traps in transects at different distances (25, 75 and 150 metres) from the path to study whether distance from hiker activity influences the number of deer detected. We compared this with the detection of red deer in an additional, spatially isolated area (one km away from any other transects and the hiking path). We collected count data on hikers at the start of the path and explored hourly (red deer detection during the day), daily, diurnal (day vs night), and monthly spatial distributions of red deer. Using Generalized Linear Mixed Models with forward model selection, we found that the distribution of deer changed with the hiking activity. We found that fewer red deer were detected during busy hourly hiking periods. We found that during the day, more red deer were detected at 150m than at 25m. Moreover, during the day, red deer were detected at a greater rate in the isolated area than around the transects close to the path and more likely to be found close to the path at night. This suggests that avoidance of hikers by red deer, in this study area, takes place over distances greater than 75m and that red deer are displaced into less disturbed areas when the hiking path is busy. Our results suggest that the impact of hikers is short-term, as deer return to the disturbed areas during the night.</p>
Spatial dataset for ecological response models and spatial distribution of Ataeniobius toweri (Cyprinodontiformes: Goodeidae) in the Media Luna spring, Mexico
<p>Dataset for the endangered endemic fish Ataeniobius toweri in the Media Luna spring, Mexico. This information includes field records for the adult and juvenile stage of the species in three sessions corresponding to the summer period, in the years 1999, 2009 and 2019.</p> <p>Our original databases are those concerning the records of the species' presence by life stage and summer period: <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/Occ_records_DOMAIN_At_Ad_09.csv?versionId=9566fb68-3948-49ca-b485-2d7aac863445">Occ_records_DOMAIN_At_Ad_09.csv</a>, <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/Occ_records_DOMAIN_At_Ad_19.csv?versionId=6f7a0f47-4990-465c-a02c-191247c395c6">Occ_records_DOMAIN_At_Ad_19.csv</a>, <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/Occ_records_DOMAIN_At_Ad_99.csv?versionId=e9158e08-bf91-4097-828f-3ce850e1bee4">Occ_records_DOMAIN_At_Ad_99.csv</a>, <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/Occ_records_DOMAIN_At_Ju_09.csv?versionId=5bd51f0e-7215-4657-bfb5-bb49e6cdd57f">Occ_records_DOMAIN_At_Ju_09.csv</a>, <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/Occ_records_DOMAIN_At_Ju_19.csv?versionId=32d0c72a-a548-406d-8694-8d939e73058e">Occ_records_DOMAIN_At_Ju_19.csv</a>, <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/Occ_records_DOMAIN_At_Ju_99.csv?versionId=d786131a-f445-44d2-a319-0a75aa666f3f">Occ_records_DOMAIN_At_Ju_99.csv</a>.</p> <p>From the above files, we generate the following dataset:</p> <p>The general basis of the species records by life stage and period: <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/Occ_records_by_sector&period_At_Ad_Ju.csv?versionId=13ffb71e-ef52-43f0-9605-e416359d5997">Occ_records_by_sector&period_At_Ad_Ju.csv</a>.</p> <p>The database that includes the 500 background points generated from records of the species' presence by life stage: <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/Pres_back_GLM_At_Ad_09.csv?versionId=7083cda2-a16d-4a25-9404-4c134adc2fa5">Pres_back_GLM_At_Ad_09.csv</a>, <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/Pres_back_GLM_At_Ad_19.csv?versionId=2cbd76f6-241d-4035-b25b-1ce490362d6a">Pres_back_GLM_At_Ad_19.csv</a>, <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/Pres_back_GLM_At_Ad_99.csv?versionId=c570be15-2b21-4a2e-9377-0e7962a26d1b">Pres_back_GLM_At_Ad_99.csv</a>, <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/Pres_back_GLM_At_Ju_09.csv?versionId=11624daf-2da0-4bb1-95d4-b3c917c94c4f">Pres_back_GLM_At_Ju_09.csv</a>, <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/Pres_back_GLM_At_Ju_19.csv?versionId=1c06bcd0-4e34-49c4-859e-100f3401e225">Pres_back_GLM_At_Ju_19.csv</a> y <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/Pres_back_GLM_At_Ju_99.csv?versionId=e10d7c51-f6f8-407c-8141-1b0196d00aed">Pres_back_GLM_At_Ju_99.csv</a>.</p> <p>The databases with the extracted values of the underwater coverage (UC) and water depth (WDp) variables from the presence and background points: <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/GBM_rel_contribution_At_Ad_09.csv?versionId=0735fbdb-5f3d-4842-ae3e-a45e849bb12f">GBM_rel_contribution_At_Ad_09.csv</a>, <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/GBM_rel_contribution_At_Ad_19.csv?versionId=3ba6c099-e807-4e07-b1ef-32a63d347a50">GBM_rel_contribution_At_Ad_19.csv</a>, <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/GBM_rel_contribution_At_Ad_99.csv?versionId=49e89e06-7740-425a-be2e-db5f38c0aeca">GBM_rel_contribution_At_Ad_99.csv</a>, <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/GBM_rel_contribution_At_Ju_09.csv?versionId=fcc857f9-4332-4e15-901e-c4deecda46ab">GBM_rel_contribution_At_Ju_09.csv</a>, <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/GBM_rel_contribution_At_Ju_19.csv?versionId=34dd733a-af86-46ad-a973-4e90e9299117">GBM_rel_contribution_At_Ju_19.csv</a> y <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/GBM_rel_contribution_At_Ju_99.csv?versionId=c7fe7fa9-e85b-45ba-9681-21f6a9cd3924">GBM_rel_contribution_At_Ju_99.csv</a>.</p> <p>The database with the results of the relative contribution for each variable by life stage and summer period: <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/Summary_GBM_At_Ad_Ju.csv?versionId=f0b0b4d4-62b0-4d95-b6f4-ec5e00b97633">Summary_GBM_At_Ad_Ju.csv</a>.</p> <p>The databases containing the correlation values between the UC and WDp variables, by Pearson's method, for each point of presence and background: <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/Pearson_corr_At_Ad_09.csv?versionId=99e74579-6edf-46e2-8f5d-3dd06a9a325f">Pearson_corr_At_Ad_09.csv</a>, <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/Pearson_corr_At_Ad_19.csv?versionId=ea4d938e-133e-426d-b11a-85ed877876f8">Pearson_corr_At_Ad_19.csv</a>, <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/Pearson_corr_At_Ad_99.csv?versionId=a499ab48-6be0-447c-9521-8e500769d4de">Pearson_corr_At_Ad_99.csv</a>, <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/Pearson_corr_At_Ju_09.csv?versionId=da583fda-0bc5-4769-bab8-bfb3c365232e">Pearson_corr_At_Ju_09.csv</a>, <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/Pearson_corr_At_Ju_19.csv?versionId=88e89cc1-2bfa-45cb-b6af-c87737a6fbd7">Pearson_corr_At_Ju_19.csv</a> y <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/Pearson_corr_At_Ju_99.csv?versionId=fddca19c-fa6b-460d-a571-966229fad955">Pearson_corr_At_Ju_99.csv</a>.</p> <p>And, the databases that contain the probability values for the generation of the ecological response curves in function of the UC variable: <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/At_Ad_09_Prob_UC.csv?versionId=240d8a0c-c0f6-4a1f-8983-74320b0863f8">At_Ad_09_Prob_UC.csv</a>, <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/At_Ad_19_Prob_UC.csv?versionId=2bff87d1-b910-4cfd-b521-881e56034b19">At_Ad_19_Prob_UC.csv</a>, <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/At_Ad_99_Prob_UC.csv?versionId=02050376-e34d-46ac-bdf9-7bc7b1f8570f">At_Ad_99_Prob_UC.csv</a>, <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/At_Ju_09_Prob_UC.csv?versionId=a3182956-bbf4-4853-a3a2-4296e7acf79c">At_Ju_09_Prob_UC.csv</a>, <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/At_Ju_19_Prob_UC.csv?versionId=21a89330-89d3-442a-a801-19842a3bed70">At_Ju_19_Prob_UC.csv</a> y <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/At_Ju_99_Prob_UC.csv?versionId=ff7d3966-6248-49d2-9000-07e374f0ba76">At_Ju_99_Prob_UC.csv</a>.</p> <p>For more information about the codes where the previous dataset was generated, visit the following repository URL: <a href="https://doi.org/10.5281/zenodo.7603557">https://doi.org/10.5281/zenodo.7603557</a>.</p> <p>Also, to download the UC and WDp variables to run the modeling processes, visit the following repository URL: <a href="https://doi.org/10.5281/zenodo.7603890">https://doi.org/10.5281/zenodo.7603890</a>.</p>
Data from: Spatial and temporal components of induced plant responses in the context of herbivore life history and impact on host
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Data from: Dynamics of bird assemblages in response to temporally and spatially variable resources in arid Australia
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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)
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.