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14 results for “distribution forecast”
Using Phenology to Forecast Species Distributions across the Eastern United States in the 2070s
Studies that use species distribution models (SDMs) to document the relationship between species’ geographic range and environmental conditions rarely consider functional traits, such as phenology, that strongly affect species’ demography and fitness. Using more than 120,000 herbarium specimens representing 360 plant species across the eastern United States, we created a novel “phenology-informed” SDM that integrates dynamic phenological responses to changing climates. Compared to standard SDMs based only on abiotic variables, our phenology-informed SDMs forecast significantly lower species habitat loss, and less species turnover within communities under climate change. These results suggest that phenotypic plasticity and/or local adaptation in phenology may help many species adjust their ecological niches and persist in their habitats during periods of rapid environmental change. By modeling historical data that link phenology, climate and species distributions, our findings reveal how species’ reproductive phenology mediates their geographic distributions along environmental gradients and affect regional biodiversity patterns under future climate changes. More importantly, our newly developed model also circumvents the need for mechanistic models that explicitly link traits to occurrences for each species, and could thus facilitate the deployment of trait-based SDMs across unprecedented spatial and taxonomic scales.
Data from: Forecasting animal distribution through individual habitat selection: Insights for population inference and transferable predictions
<p>Habitat selection models frequently use data collected from a small geographic area over a short window of time to extrapolate patterns of relative abundance to unobserved areas or periods of time. However, these types of models often poorly predict how animals will use habitat beyond the place and time of data collection because space-use behaviors vary between individuals and are context-dependent. Here, we present a modelling workflow to advance predictive distribution performance by explicitly accounting for individual variability in habitat selection behavior and dependence on environmental context. Using global positioning system (GPS) data collected from 238 individual pronghorn, (<em>Antilocapra americana</em>), across 3 years in Utah, we combine individual-year-season-specific exponential habitat-selection models with weighted mixed-effects regressions to both draw inference about the drivers of habitat selection and predict space-use in areas/times where/when pronghorn were not monitored. We found a tremendous amount of variation in both the magnitude and direction of habitat selection behavior across seasons, but also across individuals, geographic regions, and years. We were able to attribute portions of this variation to season, movement strategy, sex, and regional variability in resources, conditions, and risks. We were also able to partition residual variation into inter- and intra-individual components. We then used the results to predict population-level, spatially and temporally dynamic, habitat-selection coefficients across Utah, resulting in a temporally dynamic map of pronghorn distribution at a 30x30m resolution but an extent of 220,000km2. We believe our transferable workflow can provide managers and researchers alike a way to turn limitations of traditional habitat selection models - variability in habitat selection - into a tool to understand and predict species-habitat associations across space and time.</p>
Data from: Estimating a physiologically-based threshold to oxygen and temperature from marine monitoring data reveals challenges and opportunities for forecasting distribution shifts
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Data from: Forecasting animal distribution through individual habitat selection: Insights for population inference and transferable predictions
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The past, present, and future of predator-prey interactions in a warming world: using species distribution modeling to forecast ectotherm-endotherm niche overlap
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HRRR Convective Objects for "Comparing Distributions of Overshooting Convection in HRRR Forecasts to Observations"
<p>Data files for all convective objects identified from High-Resolution Rapid Refresh (HRRR) model forecasts for the Contiguous United States initialized at 06 UTC every day of May and July 2021. Convection was defined in the following two ways, one corresponding to each file uploaded here:</p> <ul> <li>Meeting the criteria for convection defined by the Storm Labelling in Three-Dimensions (SL3D; Starzec et al. 2017) algorithm</li> <li>Satisfying two vertical velocity thresholds (2 m/s at 4 km altitude and 5 m/s at 8 km altitude)</li> </ul>
Forecasting species distributions: Correlation does not equal causation
<p><strong>Aim:</strong> Identifying the mechanisms influencing species' distributions is critical for accurate climate change forecasts. However, current approaches are limited by correlative models that cannot distinguish between direct and indirect effects.</p> <p><strong>Location: </strong>New Hampshire and Vermont, USA.</p> <p><strong>Methods: </strong>Using causal and correlational models and new theory on range limits, we compared current (2014–2019) and future (2080s) distributions of ecologically important mammalian carnivores and competitors along range limits in the northeastern US under two global climate models (GCMs) and a high-emissions scenario (RCP8.5) of projected snow and forest biomass change.</p> <p><strong>Results:</strong> Our hypothesis that causal models of climate-mediated competition would result in different distribution predictions than correlational models, both in the current and future periods, was well-supported by our results; however, these patterns were prominent only for species pairs that exhibited strong interactions. The causal model predicted the current distribution of Canada lynx (<em>Lynx canadensis</em>) more accurately, likely because it incorporated the influence of competitive interactions mediated by snow with the closely related bobcat (<em>Lynx rufus</em>). Both modeling frameworks predicted an overall decline in lynx occurrence in the central high elevation regions and increased occurrence in the northeastern region in the 2080s due to changes in land use that provided optimal habitat. However, these losses and gains were less substantial in the causal model due to the inclusion of an indirect buffering effect of snow on lynx.</p> <p><strong>Main conclusions:</strong> Our comparative analysis indicates that a causal framework, steeped in ecological theory, can be used to generate spatially-explicit predictions of species distributions. This approach can be used to disentangle correlated predictors that have previously hampered understanding of range limits and species' response to climate change.</p>
Data from: Forecasting range shifts using abundance distributions along environmental gradients
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Data integration improves species distribution forecasts under novel ocean conditions
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Data from: Incorporating abundance information and guiding variable selection for climate-based ensemble forecasting of species' distributional shifts
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Forecasting species distributions: Correlation does not equal causation
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Supplementary material 1 from: Jarnevich CS, Young NE, Sheffels TR, Carter J, Sytsma MD, Talbert C (2017) Evaluating simplistic methods to understand current distributions and forecast distribution changes under climate change scenarios: an example with coypu (Myocastor coypus). NeoBiota 32: 107-125. https://doi.org/10.3897/neobiota.32.8884
Supplementary figures and table : Explanation note: Supporting information including global location data used to create models (Supplementary figure 1), global distribution of tropical environments (Supplementary figure 2), and Global circulation model climate data used for forecasts of Myocastor coypus distributions (Supplementary table 1).
Dataset and code for A Multidimensional Error Verification Method for Weather Forecast Based on the Spatial Distribution Structural Similarity of Meteorological Elements
<p>The dataset includes rainfall observations and mesoscale numerical prediction data for 16 July 2019, 13 June 2020 and 2024 rainy season.<br>The code is the algorithm procedure of this paper. Among them, meiyutest.py is the rainfall scoring procedure for the Meiyu period, and 2019case is the forecast scoring and plotting procedure for two individual cases.</p>
A new method for runoff forecasting in ungauged areas based on the Xinanjiang model incorporating multiple spatial geographical distribution data
<p>This dataset includes two parts:</p> <p>(1) The SHP format layer data of 256 watersheds in North America and 2 watersheds in ChinaThe SHP format layer data of 256 watersheds in North America and 2 watersheds in China.</p> <p>(2) A reference meta-watershed database of spatial data was established, covering a spatial database of 256 watersheds. This provides a data foundation for runoff forecasting in other ungauged watersheds.</p>
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