Filters: Tags: species distribution modeling (X) > Date Range: {"choice":"year"} (X)
11 results (38ms)|
Filters
Date Types (for Date Range)
Types Contacts
Categories Tag Types
|
This dataset contains predictions of habitat suitability of reed canarygrass (Phalaris arundinacea) in Upper Mississippi River floodplain forest understories from Pool 3 to Pool 13. Predictions were created using three machine learning algorithms (Bayesian additive regression trees, boosted trees, and random forest). This dataset contains rasters that provide habitat suitability predictions for each 12m raster cell that had forested landcover in 2010. In addition to one raster for each of the three algorithms an ensemble (mean prediction of all three algorithms) prediction raster for each pool is provided. The presence/absence observations used to train the model are contained in a .csv file with each plot location....
This project used species distribution modeling, population genetics, and geospatial analysis of historical vs. modern vertebrate populations to identify climate change refugia and population connectivity across the Sierra Nevada. It is hypothesized that climate change refugia will increase persistence and stability of populations and, as a result, maintain higher genetic diversity. This work helps managers assess the need to include connectivity and refugia in climate change adaptation strategies. Results help Sierra Nevada land managers allocate limited resources, aid future scenario assessment at landscape scales, and develop a performance measure for assessing resilience.
Categories: Data,
Project;
Tags: 2011,
2013,
CA,
California Landscape Conservation Cooperative,
Conservation Design,
We developed habitat suitability models for three invasive plant species: stiltgrass (Microstegium vimineum), sericea lespedeza (Lespedeza cuneata), and privet (Ligustrum sinense). We applied the modeling workflow developed in Young et al. 2020, developing similar models for occurrence data, but also models trained using species locations with percent cover ≥10%, ≥25%, and ≥50%. We chose predictors from a national library of environmental variables known to physiologically limit plant distributions (Engelstad et al. 2022 Table S1) and relied on human input based on natural history knowledge to further narrow the variable set for each species before developing habitat suitability models. We developed models using...
This product used species distribution modeling (SDM) to model the geographic distribution fire promoting grasses across the islands of Hawaii under both current climate conditions and under future climate change scenarios (RCP 8.5 at year 2100). The RCP 8.5 scenario assumes unmitigated and continued release of greenhouse grasses and continued human population growth. Six species of well established and widely distributed grasses (Andropogon virginicus (broomsedge), Cenchrus ciliaris (buffelgrass), Cenchrus setaceus (fountain grass), Megathyrus maximus (guinea grass, Urochloa maxima, Pancicum maximum), Melinis minutiflora (mollasses grass), and Schizachyrium microstachyum (formerly referred to as S. condensatum...
Categories: Data;
Types: Downloadable,
GeoTIFF,
Map Service,
Raster;
Tags: Hawaii,
Species distribution modeling,
biota,
climate change,
fire,
An important component in the fields of ecology and conservation biology is understanding the environmental conditions and geographic areas that are suitable for a given species to inhabit. A common tool in determining such areas is species distribution modeling which uses computer algorithms to determine the spatial distribution of organisms. Most commonly the correlative relationships between the organism and environmental variables are the primary consideration. The data requirements for this type of modeling consist of known presence and possibly absence locations of the species as well as the values of environmental or climatic covariates thought to define the species habitat suitability at these locations....
Categories: Publication;
Types: Citation;
Tags: North Central CASC,
Software for Assisted Species Modeling (SAHM),
VisTrails,
species distribution modeling
The CA Academy of Science and Point Blue Conservation Science conducted a systematic analysis of uncertainty in modeling the future distributions of ~50 California endemic plant species and ~50 California land birds, explicitly partitioning among 5 alternative sources of variation and testing for their respective contributions to overall variation among modeled outcomes. They mapped the uncertainty from identified sources, which can guide decisions about monitoring, restoration, acquisition, infrastructure, etc., in relation to climate change.
Categories: Data,
Project;
Tags: 2011,
Academics & scientific researchers,
CA,
California,
California Landscape Conservation Cooperative,
There is a broad consensus within the scientific community that global climate is undergoing a comparatively rapid change. Since many plants and animals depend on specific types of climate, it is imperative to understand: (1) the details of species’ climatic preferences; (2) how climates may change in the future; and (3) how species may respond to these changes. Species distribution modeling (SDM) is an increasingly important tool to address conservation biology and global change issues. As Fortini and colleagues described in their largest vulnerability assessment in the US, SDMs provide critical information on biological refuges and potential future shifts in species ranges. In addition, climate changes could alter...
Categories: Publication;
Types: Citation;
Tags: Hawai'i,
Pacific Islands CASC,
Plants,
Vegetation,
Wildlife and Plants,
This is a dataset containing the potential distribution of Japanese brome (Bromus japonicus). We developed habitat suitability models for Japanese brome, as suggested by Department of Interior land management agencies. We applied the modeling workflow developed in Young et al. 2020 to species not included in the original case studies. Our methodology balanced trade-offs between developing highly customized models for a few species versus fitting non-specific and generic models for numerous species. We developed a national library of environmental variables known to physiologically limit plant distributions (Engelstad et al. 2022 Table S1: https://doi.org/10.1371/journal.pone.0263056) and relied on human input based...
We developed habitat suitability models for invasive plant species selected by Department of Interior land management agencies. We applied the modeling workflow developed in Young et al. 2020 to species not included in the original case studies. Our methodology balanced trade-offs between developing highly customized models for a few species versus fitting non-specific and generic models for numerous species. We developed a national library of environmental variables known to physiologically limit plant distributions (Engelstad et al. 2022 Table S1) and relied on human input based on natural history knowledge to further narrow the variable set for each species before developing habitat suitability models. We developed...
This is a dataset containing aggregated non-native plant occurrence and abundance data for the contiguous United States. We used these data to develop habitat suitability models for species found in the Eastern United States using locations with 5% cover or greater. We adapted the INHABIT modeling workflow (Young et al. 2020), using a consistent set of climatic predictors that were important in the INHABIT models. We developed models using five algorithms with VisTrails: Software for Assisted Habitat Modeling [SAHM 2.2.2]. We accounted for sampling bias by using the target background approach, and constructed model ensembles using the five models for each species for three different thresholds (conservative to targeted;1st...
This is a dataset containing aggregated non-native plant occurrence and abundance data for the contiguous United States. We used these data to develop habitat suitability models for species found in the Eastern United States using locations with 5% cover or greater. We adapted the INHABIT modeling workflow (Young et al. 2020), using a consistent set of climatic predictors that were important in the INHABIT models. We developed models using five algorithms with VisTrails: Software for Assisted Habitat Modeling [SAHM 2.2.2]. We accounted for sampling bias by using the target background approach, and constructed model ensembles using the five models for each species for three different thresholds (conservative to targeted;1st...
Categories: Data;
Tags: Botany,
Contiguous United States,
Ecology,
SAHM,
Species Distribution Modeling,
|
|
