From 8f8dd135abf2129b4d86a267eda8f081b9ad280a Mon Sep 17 00:00:00 2001 From: frousseu Date: Fri, 6 Oct 2023 11:47:29 -0400 Subject: [PATCH 1/2] updated pipeline description --- pipelines/SDM/SDM_maxEnt_outputs.json | 134 ++++++++++++++++++-------- 1 file changed, 95 insertions(+), 39 deletions(-) diff --git a/pipelines/SDM/SDM_maxEnt_outputs.json b/pipelines/SDM/SDM_maxEnt_outputs.json index 26d3c991..2cb06715 100644 --- a/pipelines/SDM/SDM_maxEnt_outputs.json +++ b/pipelines/SDM/SDM_maxEnt_outputs.json @@ -68,7 +68,11 @@ "data": { "type": "options", "value": "bootstrap", - "options": ["bootstrap", "crossvalidation", "none"] + "options": [ + "bootstrap", + "crossvalidation", + "none" + ] } }, { @@ -133,7 +137,12 @@ "dragHandle": ".dragHandle", "data": { "type": "float[]", - "value": [-2316297, -1971146, 1015207, 1511916] + "value": [ + -2316297, + -1971146, + 1015207, + 1511916 + ] } }, { @@ -215,7 +224,13 @@ "data": { "type": "options", "value": "vif.cor", - "options": ["vif.cor", "vif.step", "pearson", "spearman", "kendall"] + "options": [ + "vif.cor", + "vif.step", + "pearson", + "spearman", + "kendall" + ] } }, { @@ -229,7 +244,11 @@ "data": { "type": "options", "value": "pearson", - "options": ["pearson", "spearman", "kendall"] + "options": [ + "pearson", + "spearman", + "kendall" + ] } }, { @@ -293,7 +312,12 @@ "data": { "type": "options", "value": "bbox", - "options": ["box", "mcp", "buffer", "bbox"] + "options": [ + "box", + "mcp", + "buffer", + "bbox" + ] } }, { @@ -341,7 +365,11 @@ "dragHandle": ".dragHandle", "data": { "type": "text[]", - "value": ["L", "LQ", "LQHP"] + "value": [ + "L", + "LQ", + "LQHP" + ] } }, { @@ -354,7 +382,11 @@ "dragHandle": ".dragHandle", "data": { "type": "float[]", - "value": [0.5, 1, 2] + "value": [ + 0.5, + 1, + 2 + ] } }, { @@ -368,7 +400,11 @@ "data": { "type": "options", "value": "AUC", - "options": ["p10", "AIC", "AUC"] + "options": [ + "p10", + "AIC", + "AUC" + ] } }, { @@ -382,7 +418,12 @@ "data": { "type": "options", "value": "lat_lon", - "options": ["lat_lon", "lon_lat", "lon_lon", "lat_lat"] + "options": [ + "lat_lon", + "lon_lat", + "lon_lon", + "lat_lat" + ] } }, { @@ -419,7 +460,9 @@ "dragHandle": ".dragHandle", "data": { "type": "text[]", - "value": ["Acer saccharum"] + "value": [ + "Acer saccharum" + ] } }, { @@ -840,7 +883,12 @@ "label": "bbox", "description": "Vector of float, bbox coordinates of the bbox in the order xmin, ymin, xmax, ymax", "type": "float[]", - "example": [-2316297, -1971146, 1015207, 1511916] + "example": [ + -2316297, + -1971146, + 1015207, + 1511916 + ] }, "data>heatmapFromSTAC.yml@67|taxa": { "description": "taxonomic group to retrieve GBIF heatmap", @@ -861,7 +909,10 @@ "description": "Source of the data (One of gbif_pc - Planetary computer or gbif_api - GBIF Download API)", "label": "Data source", "type": "options", - "options": ["gbif_pc", "gbif_api"], + "options": [ + "gbif_pc", + "gbif_api" + ], "example": "gbif_api" }, "data>pyLoadObservations>pyLoadObservations.yml@96|min_year": { @@ -899,13 +950,16 @@ "description": "Vector of strings, collection name followed by '|' followed by item id", "label": "collections_items", "type": "text[]", - "example": ["chelsa-clim|bio1", "chelsa-clim|bio2"] + "example": [ + "chelsa-clim|bio1", + "chelsa-clim|bio2" + ] }, "data>loadFromStac.yml@119|spatial_res": { "description": "Integer, spatial resolution of the rasters", "label": "spatial resolution", "type": "float", - "example": 1000.0 + "example": 1000 }, "data>loadFromStac.yml@119|mask": { "description": "Shapefile, used to mask the output rasters", @@ -916,50 +970,51 @@ "label": "Taxa list", "description": "Array of taxa values", "type": "text[]", - "example": ["Acer saccharum"] + "example": [ + "Acer saccharum" + ] } }, "outputs": { - "pipeline@121": { - "label": "Species name", - "description": "Species for which the distribution model is generated", - "weight": 1 + "SDM>rangePredictions.yml@68|range_predictions": { + "description": "Variability of predictions based on range method", + "label": "Variability of predictions", + "type": "image/tiff;application=geotiff", + "weight": 6 }, + "SDM>runMaxent.yml@108|sdm_pred": { + "description": "Model predictions from Maxent algorithm", + "label": "Predictions", + "type": "image/tiff;application=geotiff", + "range": [ + 0, + 1 + ], + "weight": 5 + }, + "pipeline@121": {}, "filtering>cleanCoordinates.yml@34|clean_presence": { "description": "Occurrences from GBIF after cleaning", "label": "Presences", "type": "text/tab-separated-values", "weight": 2 }, - "data>heatmapFromSTAC.yml@67|raster": { - "description": "Heatmap of GBIF occurences for plants used for bias correction", - "label": "Density of GBIF occurrences", - "type": "image/tiff;application=geotiff", - "weight": 4 - }, "SDM>removeCollinearity.yml@97|rasters_selected": { "description": "Environmental layers used as predictors in species distribution modeling", "label": "Environmental predictors", "type": "image/tiff;application=geotiff[]", "weight": 3 }, - "SDM>runMaxent.yml@108|sdm_pred": { - "description": "Model predictions from Maxent algorithm", - "label": "Predictions", - "type": "image/tiff;application=geotiff", - "range": [0, 1], - "weight": 5 - }, - "SDM>rangePredictions.yml@68|range_predictions": { - "description": "Variability of predictions based on range method", - "label": "Variability of predictions", + "data>heatmapFromSTAC.yml@67|raster": { + "description": "Heatmap of GBIF occurences for plants used for bias correction", + "label": "Density of GBIF occurrences", "type": "image/tiff;application=geotiff", - "weight": 6 + "weight": 4 } }, "metadata": { "name": "Species distribution modeling with Maxent", - "description": "This pipeline generates predictions for a species distribution model using the Maxent algorithm. Bias correction is achieved using random pseudo-absences throught the region of interest. A variance map to represent the prediction uncertainty is generated through bootstraping.", + "description": "This pipeline generates predictions for a species distribution model using the Maxent algorithm. Several background methods are possible, including randomly distributed pseudo-absences throughout the region, background thickening and target-group background selection. Bias correction is achieved using the target-group background selection method. A variance map to represent the prediction uncertainty is generated through bootstraping.", "author": [ { "name": "Sarah Valentin", @@ -970,10 +1025,11 @@ "identifier": "https://orcid.org/0000-0002-5967-9156" }, { - "name": "François Rousseu" + "name": "François Rousseu", + "identifier": "https://orcid.org/0000-0002-2400-2479" } ], "license": "MIT", "external_link": "https://github.com/GEO-BON/biab-2.0/blob/main/scripts/SDM/runMaxent.R" } -} +} \ No newline at end of file From a1c99c28cb062bd42dfbf5913ad7f3ee4d1ec527 Mon Sep 17 00:00:00 2001 From: frousseu Date: Fri, 6 Oct 2023 12:24:46 -0400 Subject: [PATCH 2/2] added some refs --- pipelines/SDM/SDM_maxEnt_outputs.json | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pipelines/SDM/SDM_maxEnt_outputs.json b/pipelines/SDM/SDM_maxEnt_outputs.json index 2cb06715..41ba9334 100644 --- a/pipelines/SDM/SDM_maxEnt_outputs.json +++ b/pipelines/SDM/SDM_maxEnt_outputs.json @@ -1014,7 +1014,7 @@ }, "metadata": { "name": "Species distribution modeling with Maxent", - "description": "This pipeline generates predictions for a species distribution model using the Maxent algorithm. Several background methods are possible, including randomly distributed pseudo-absences throughout the region, background thickening and target-group background selection. Bias correction is achieved using the target-group background selection method. A variance map to represent the prediction uncertainty is generated through bootstraping.", + "description": "This pipeline generates predictions for a species distribution model using the Maxent algorithm. Several background methods are possible, including randomly distributed pseudo-absences throughout the region, background thickening (Vollering et al. 2019, [https://doi.org/10.1111/ecog.04503] and target-group background selection (Phillips et al. 2009, [https://doi.org/10.1890/07-2153.1]). Bias correction is achieved using the target-group background selection method. A variance map to represent the prediction uncertainty is generated through bootstraping.", "author": [ { "name": "Sarah Valentin",