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* Create SHI.md * Create SDM.md * Added SHI link to GEO BON
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| Species distributions are an important EBV in the ‘species populations’ class. Knowing where species are is essential for understanding biodiversity patterns and informing conservation efforts. However, less than 10% of the world is well sampled, and even the longest running and well-sampled biodiversity observation networks have substantial data gaps. Information on species occurrences is often sparse and heavily spatially and taxonomically biased, necessitating the need for species distribution models (SDMs) to fill these data gaps and provide a better, less biased idea of where species are. SDM outputs be used as key base layers for a wide variety of purposes including: creating maps for sampling prioritization, quantifying the impact of environmental stressors on species, mapping habitat suitability for at-risk species, mapping biodiversity hotspots across the landscape, identifying the locations of conservation priorities and protected area expansion, identifying sampling gaps and the needed locations of future sampling, and calculating a range of biodiversity indicators including the Species Habitat Index (SHI), the Species Protection Index (SPI) | ||
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| ### **MaxEnt** | ||
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| **Methods:** | ||
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| SDMs predict where species are likely to occur based on a suite of environmental variables that are associated with known occurrences (Peterson, 2001; Elith and Leathwick, 2009). The MaxEnt pipeline pulls occurrences of the species of interest from GBIF and environmental raster layers from the GEO BON STAC catalog. Then, the pipeline cleans the GBIF data by only including one occurrence per pixel and removes collinearity between the environmental layers. Third, the pipeline creates a set of pseudo-absences (background points) and combines this with presences and the environmental predictors to create a dataset that is ready to be input into the SDM model. The pipeline runs the SDM on this data using the MaxEnt algorithm using the ENMeval R package (Kass et al. 2021). The MaxEnt SDM is run by 1\) partitioning occurrence and background points into subsets for training and evaluation, 2\) building the model with different algorithmic settings (model tuning), and 3\) evaluating their performance ([see package vignette](https://jamiemkass.github.io/ENMeval/articles/ENMeval-2.0-vignette.html#partition)). Lastly, the pipeline computes the 95% confidence interval using bootstrapping and cross validation techniques. | ||
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| **BON in a Box pipeline:** | ||
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| The BON in a Box pipeline allows you to run an SDM for a specific region and species (or multiple species) of interest. The pipeline has the following inputs: | ||
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| * **Taxa list:** The user can specify the species (or multiple species) they are interested in. | ||
| * **Bounding box:** The user can specify the bounding box where they want to distribution to be predicted (units must be in the chosen CRS). | ||
| * **Projection system:** The user can specify a projection system. | ||
| * **Data source:** The user can pull species’ occurrences using the GBIF API or from GBIF on the planetary computer. | ||
| * **Environmental layers:** The user specifies the environmental layers that they want to include in the species distribution model, pulled from a STAC catalog. | ||
| * **Minimum and maximum year:** The user can specify the year range for which they want to pull GBIF observations. | ||
| * **Method background:** The user chooses a method to sample background points (pseudo absences) from a drop down menu | ||
| * **Number of background points:** The user specifies the number of background points to choose | ||
| * **Number of runs:** The number of SDMs to run to compute the 95% confidence interval through cross validation. | ||
| * **Partition type:** The user can choose a method for partitioning the occurrence and background data into subsets for training and evaluation from a dropdown menu | ||
| * Block \- partitions the bounding box into four equally sized quadrants and assigns groups by quadrant | ||
| * Checkerboard 1 \- Generates checkerboard from the study area and assigns groups based on what square the points fall in | ||
| * Checkerboard 2- Similar to checkerboard 1 but performs this separately for occurrence and background points | ||
| * Jackknife \- Does not partition the background points into testing and training (uses them all), performs leave one out cross validation (recommended for small datasets only) | ||
| * Random k-fold \- Does not partition the background points into testing and training, partitions groups randomly into a user specified (K) number of bins, and runs the model k times, with each bin used once as testing. | ||
| * **Mask:** If the user is only interested in a specific country or study area, they can upload a polygon and the pipeline will crop the results to only that area. | ||
| * **Spatial resolution:** The spatial resolution at which to predict the SDMs. | ||
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| The pipeline creates the following outputs: | ||
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| * **DOI of GBIF download:** Generates a DOI of the GBIF download for reproducibility. | ||
| * **Presences:** GBIF presences can be viewed on a map. | ||
| * **Environmental Predictors:** All environmental layers can be viewed separately as rasters. | ||
| * **Predictions:** SDM prediction probabilities can be viewed as a raster. | ||
| * **Variability of predictions:** The variability of the 95% confidence of each prediction can be viewed on a map to show uncertainty. | ||
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| **Contributors:** | ||
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| * [Sarah Valentin](https://orcid.org/0000-0002-9028-681X) | ||
| * [Guillaume Larocque](https://orcid.org/0000-0002-5967-9156) | ||
| * [François Rousseu](https://orcid.org/0000-0002-2400-2479) | ||
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| **Citations:** | ||
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| Elith, J., & Leathwick, J. R. (2009). Species Distribution Models: Ecological Explanation and Prediction Across Space and Time. Annual Review of Ecology, Evolution, and Systematics, 40(Volume 40, 2009), 677–697. https://doi.org/10.1146/annurev.ecolsys.110308.120159 | ||
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| Kass JM, Muscarella R, Galante PJ, Bohl CL, Pinilla-Buitrago GE, Boria RA, Soley-Guardia M, Anderson RP (2021). “ENMeval 2.0: Redesigned for customizable and reproducible modeling of species’ niches and distributions.” Methods in Ecology and Evolution, 12(9), 1602-1608. https://doi.org/10.1111/2041-210X.13628. | ||
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| Peterson, A. T. (2001). Predicting Species’ Geographic Distributions Based on Ecological Niche Modeling. The Condor, 103(3), 599–605. [https://doi.org/10.1093/condor/103.3.599](https://doi.org/10.1093/condor/103.3.599) |
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| The [Species Habitat Index](https://geobon.org/ebvs/indicators/species-habitat-index-shi/) (SHI) is a component indicator for the Global Biodiversity Framework (GBF). SHI measures changes in ecological integrity by measuring the change in the quality and connectivity of habitats for species of interest. SHI is an important indicator for assessing progress towards Goal A of the GBF, which calls for the enhanced integrity of natural ecosystems. Read more about how SHI can be used to assess progress toward goal A here ([https://cdn.mol.org/static/files/indicators/habitat/WCMC-species\_habitat\_index-15Feb2022.pdf](https://cdn.mol.org/static/files/indicators/habitat/WCMC-species_habitat_index-15Feb2022.pdf)). | ||
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| **Methods:** | ||
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| The Species Habitat Index (SHI) is the measurement of change in area and connectivity of suitable areas relative to a baseline. It is a composite of Species Habitat Scores (SHS), which measure the change in suitable area for a single species of interest. It is calculated in the BON in a Box pipeline by taking species range maps, information about elevational ranges and IUCN habitat categories to determine the suitable area for the species. Then, the pipeline uses the Global Forest Watch (GFW) data (other land cover layers and options for inputting user data will soon be added) to calculate the area and connectivity scores by species. For the area score using GFW data, only forest species are recommended to be used and a starting year no earlier than 2000\. Forest loss is detected by year and subtracted from the initial 2000 forest layer distributed within the range map of the species and filtered by elevation ranges. This layer is then used to create a raster with the distances to habitat edges and the mean value for the area is used as the connectivity score. The habitat and connectivity score are combined to form the SHS. To calculate SHI, the SHS for each species is averaged and to calculate Steward’s SHI, this is also weighted by the proportion of the species’ range that is in the study area. | ||
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| **BON in a Box pipeline:** | ||
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| BON in a Box has a pipeline to calculate SHS and SHI for species, countries, and regions of interest. The pipeline has the following user inputs: | ||
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| * **Study area:** the user can specify a country and state/province of interest or upload a custom study area polygon. | ||
| * **Study area buffer:** To avoid having edge effects in calculations, a buffer is used to the limit the area of study. It is calculated by assuming the study area is approximately a circle and is equivalent to half the radius. | ||
| * **Species:** The user can input the scientific name of a single species or multiple species of interest. Note that the SHS pipeline can only accept one species. | ||
| * **Range map:** The user can choose to extract species range maps from IUCN, Map of Life, the Quebec species database (Quebec only) or upload a custom range map as a raster file. The type of range map file can also be specified. | ||
| * **Spatial reference system:** The user can specify a CRS that is most accurate for their study area. | ||
| * **Min and max forest:** The user specifies the forest cover that is preferred by the species. | ||
| * **Initial time, final time, and time step:** The user can specify what year they want to use as the reference year, the time interval at which they want to measure SHI, and the final year. | ||
| * **Output spatial resolution:** The user can specify the spatial resolution at which they want to measure habitat change | ||
| * **Filter for elevation:** The user can decide whether they want to include elevation in the range map of the species of interest. If “yes” is chosen, the pipeline will extract the species elevation preferences from IUCN and remove areas within the range map that are outside of the elevational range of the species. The user can also specify a buffer to the elevation values. | ||
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| The pipeline creates the following outputs: | ||
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| * **SHS table:** The user can download tables of the SHS over time as csv files. | ||
| * **SHI table:** The user can view the time series of SHI values and download results as a csv. | ||
| * **SHS time series plot:** Plot of the connectivity score, habitat score, and SHS over time. The pipeline outputs separate plots for each species. | ||
| * **SHI time series plot:** Plot of the composite SHI score over time. | ||
| * **Steward’s SHI time series plot:** Plot of Steward’s SHI over time. Steward’s SHI is weighted by the proportion of the species’ range that is in the study area. | ||
| * **Habitat by time step:** Raster maps of the habitat for each time point. | ||
| * **Raster plot of forest change:** Maps areas that have lost habitat, gained habitat, and experienced no change over the specified time period. | ||
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| See an example SHI output here: (coming soon) | ||
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| Workflow for Species Habitat Score | ||
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| Multiple SHS are then combined into a Species Habitat Index | ||
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| **Contributors:** | ||
| - Maria Isabel Arce-Plata | ||
| - Guillaume Larocque | ||
| - Jaime Burbano-Girón | ||
| - Maria Camila Díaz | ||
| - Timothée Poisot | ||
| - Jory Griffith | ||
| - Jean-Michel Lord | ||
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| **References:** | ||
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| Brooks, T. M., Pimm, S. L., Akçakaya, H. R., Buchanan, G. M., Butchart, S. H. M., Foden, W., Hilton-Taylor, C., Hoffmann, M., Jenkins, C. N., Joppa, L., Li, B. V., Menon, V., Ocampo-Peñuela, N., & Rondinini, C. (2019). Measuring Terrestrial Area of Habitat (AOH) and Its Utility for the IUCN Red List. Trends in Ecology & Evolution, 34(11), 977–986. [https://doi.org/10.1016/j.tree.2019.06.009](https://doi.org/10.1016/j.tree.2019.06.009) | ||
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| Jetz, W., McGowan, J., Rinnan, D. S., Possingham, H. P., Visconti, P., O’Donnell, B., & Londoño-Murcia, M. C. (2022). Include biodiversity representation indicators in area-based conservation targets. Nature Ecology & Evolution, 6(2), 123–126. [https://doi.org/10.1038/s41559-021-01620-y](https://doi.org/10.1038/s41559-021-01620-y) |