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Dataset results
8 results for “spatial landscape variability”
Code: A model of wild bee populations accounting for spatial heterogeneity and climate induced temporal variability of food resources at the landscape level
<p><span>The viability of wild bee populations and the pollination services that they provide are driven by the availability of food resources during their activity period and within the surroundings of their nesting sites. Changes in climate and land use influence the availability of these resources and are major threats to declining bee populations. Because wild bees may be vulnerable to interactions between these threats, spatially explicit models of population dynamics that capture how bee populations jointly respond to land use at a landscape scale and weather are needed. Here, we developed a spatially and temporally explicit theoretical model of wild bee populations aiming for a middle ground between the existing mapping of visitation rates using foraging equations and more refined agent-based modelling. The model is developed for <em>Bombus</em> sp. and captures within-season colony dynamics. The model describes mechanistically foraging at the colony level and temporal population dynamics for an average colony at the landscape level. Stages in population dynamics are temperature-dependent triggered with a theoretical generalized seasonal progression, which can be informed by growing degree days (GDD). The purpose of the LandscapePhenoBee model is to evaluate the impact of systematic changes and within-season variability in resources on bee population sizes and crop visitation rates. In a simulation study, we used the model to evaluate the impact of the shortage of food resources in the landscape arising from extreme drought events in different types of landscapes (ranging from different proportions of semi-natural habitats and early and late flowering crops) on bumblebee populations.</span></p>
Code: A model of wild bee populations accounting for spatial heterogeneity and climate induced temporal variability of food resources at the landscape level
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Data from: Spatial variability in soil organic carbon in a tropical montane landscape: associations between soil organic carbon and land use, soil properties, vegetation, and topography vary across plot to landscape scales
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Spatial variability of hosts, parasitoids and their interactions across a homogeneous landscape
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Data set for "Overstory dynamics regulate the spatial variability in forest-floor CO2 fluxes across a managed boreal forest landscape"
<p>This data set is a compilation of forest stand characteristics, forest-floor environmental conditions, soil properties, ecosystem carbon stocks, and annual forest-floor CO<sub>2</sub> fluxes. The data set is composed by 3-year mean annual values obtained from biometric- and chamber-based flux measurements conducted during the period 2016–2018. Negative values of forest-floor CO<sub>2</sub> fluxes indicate carbon uptake and positive values indicate carbon release. Data were collected in 50 forest stands within the Krycklan Catchment Study (<a href="https://www.slu.se/Krycklan">https://www.slu.se/Krycklan</a>), a multi-scale long-term monitored boreal catchment spanning 68 km<sup>2</sup> in northern Sweden. Selected forest stands encompassed different soil types (sediment vs. till), dominant tree species (pine vs. spruce), and age classes (from initiation to old-growth stands).</p> <p>Variables, units, and definitions are found in the 1_metadata_Martínez-García_et_al._forest-floor_CO2_fluxes.xlsx file</p> <p>More details can be found in Martínez-García et al. (2022) Overstory dynamics regulate the spatial variability in forest-floor CO<sub>2</sub> fluxes across a managed boreal forest landscape. Agricultural and Forest Meteorology. 318: 108916. <a href="https://doi.org/10.1016/j.agrformet.2022.108916">https://doi.org/10.1016/j.agrformet.2022.108916</a></p> <p>Contact information:</p> <p>Ph.D. Eduardo Martínez García (<a href="mailto:eduardo.martinez@slu.se">eduardo.martinez@slu.se</a>, <a href="mailto:edu.martinez.garcia@gmail.com">edu.martinez.garcia@gmail.com</a>)</p> <p>Professor Matthias Peichl (<a href="mailto:matthias.peichl@slu.se">matthias.peichl@slu.se</a>)</p> <p>Department of Forest Ecology and Management, Swedish University of Agricultural Sciences (SLU), Skogsmarksgränd 17, SE-901 83, Umeå, Sweden</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>
Age-specific habitat preference, carrying capacity, and landscape structure determine the response of population spatial variability to fishing-driven age truncation
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Supplementary Tables S1 and S2 - Wild bird densities and landscape variables predict spatial patterns in HPAI outbreak risk across the Netherlands
<p>Table S1: Overview of candidate variables in order of (Rank) decreasing feature importance, including a selection of high risk bird species for HPAIV infection and land cover variables.</p> <p>Table S2: Aggregation of land cover classes to 5 major classes used in the random forest algorithms.</p>
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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.