Agricultural margins could enhance landscape connectivity for pollinating insects across the Central Valley of California, U.S.A.
Data files
Jan 16, 2023 version files 2.51 GB
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Dilts_etal_2022_landcover.tfw
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Dilts_etal_2022_landcover.tif
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Dilts_etal_2022_landcover.tif.aux.xml
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Dilts_etal_2022_landcover.tif.ovr
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Dilts_etal_2022_landcover.tif.vat.cpg
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Dilts_etal_2022_landcover.tif.vat.dbf
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Dilts_etal_2022_landcover.tif.vat.dbf.xml
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Dilts_etal_2022_lcp_closeup_current.CPG
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Dilts_etal_2022_lcp_closeup_current.dbf
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Dilts_etal_2022_lcp_closeup_current.prj
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Dilts_etal_2022_lcp_closeup_current.sbn
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Dilts_etal_2022_lcp_closeup_current.sbx
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Dilts_etal_2022_lcp_closeup_current.shp
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Dilts_etal_2022_lcp_closeup_current.shp.xml
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Dilts_etal_2022_lcp_closeup_current.shx
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Dilts_etal_2022_lcp_closeup_natural.CPG
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Dilts_etal_2022_lcp_closeup_natural.dbf
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Dilts_etal_2022_lcp_closeup_natural.prj
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Dilts_etal_2022_lcp_closeup_natural.sbn
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Dilts_etal_2022_lcp_closeup_natural.sbx
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Dilts_etal_2022_lcp_closeup_natural.shp
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Dilts_etal_2022_lcp_closeup_natural.shp.xml
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Dilts_etal_2022_lcp_closeup_natural.shx
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Dilts_etal_2022_lcp_closeup_nomargins.CPG
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Dilts_etal_2022_lcp_closeup_nomargins.dbf
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Dilts_etal_2022_lcp_closeup_nomargins.prj
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Dilts_etal_2022_lcp_closeup_nomargins.sbn
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Dilts_etal_2022_lcp_closeup_nomargins.sbx
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Dilts_etal_2022_lcp_closeup_nomargins.shp
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Dilts_etal_2022_lcp_closeup_nomargins.shp.xml
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Dilts_etal_2022_lcp_closeup_nomargins.shx
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Dilts_etal_2022_lcp_current.cpg
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Dilts_etal_2022_lcp_current.dbf
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Dilts_etal_2022_lcp_current.prj
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Dilts_etal_2022_lcp_current.sbn
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Dilts_etal_2022_lcp_current.sbx
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Dilts_etal_2022_lcp_current.shp
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Dilts_etal_2022_lcp_current.shp.xml
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Dilts_etal_2022_lcp_current.shx
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Dilts_etal_2022_lcp_natural.CPG
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Dilts_etal_2022_lcp_natural.dbf
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Dilts_etal_2022_lcp_natural.prj
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Dilts_etal_2022_lcp_natural.sbn
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Dilts_etal_2022_lcp_natural.sbx
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Dilts_etal_2022_lcp_natural.shp
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Dilts_etal_2022_lcp_natural.shp.xml
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Dilts_etal_2022_lcp_natural.shx
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Dilts_etal_2022_lcp_nomargins.CPG
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Dilts_etal_2022_lcp_nomargins.dbf
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Dilts_etal_2022_lcp_nomargins.prj
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Dilts_etal_2022_lcp_nomargins.sbn
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Dilts_etal_2022_lcp_nomargins.sbx
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Dilts_etal_2022_lcp_nomargins.shp
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Dilts_etal_2022_lcp_nomargins.shp.xml
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Dilts_etal_2022_lcp_nomargins.shx
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Dilts_etal_2022_pesticide_data.csv
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Dilts_etal_2022_pesticides.csv
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Dilts_etal_2022_resistance_high_current.tfw
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Dilts_etal_2022_resistance_high_current.tif
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Dilts_etal_2022_resistance_high_current.tif.aux.xml
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Dilts_etal_2022_resistance_high_current.tif.ovr
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Dilts_etal_2022_resistance_high_current.tif.xml
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Dilts_etal_2022_resistance_high_natural.tfw
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Dilts_etal_2022_resistance_high_natural.tif
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Dilts_etal_2022_resistance_high_natural.tif.aux.xml
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Dilts_etal_2022_resistance_high_natural.tif.ovr
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Dilts_etal_2022_resistance_high_natural.tif.vat.cpg
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Dilts_etal_2022_resistance_high_natural.tif.vat.dbf
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Dilts_etal_2022_resistance_high_natural.tif.xml
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Dilts_etal_2022_resistance_high_nomargins.tfw
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Dilts_etal_2022_resistance_high_nomargins.tif
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Dilts_etal_2022_resistance_high_nomargins.tif.aux.xml
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Dilts_etal_2022_resistance_high_nomargins.tif.ovr
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Dilts_etal_2022_resistance_high_nomargins.tif.xml
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Dilts_etal_2022_resistance_low_current.tfw
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Dilts_etal_2022_resistance_low_current.tif
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Dilts_etal_2022_resistance_low_current.tif.aux.xml
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Dilts_etal_2022_resistance_low_current.tif.ovr
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Dilts_etal_2022_resistance_low_current.tif.xml
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Dilts_etal_2022_resistance_low_natural.tfw
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Dilts_etal_2022_resistance_low_natural.tif
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Dilts_etal_2022_resistance_low_natural.tif.aux.xml
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Dilts_etal_2022_resistance_low_natural.tif.ovr
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Dilts_etal_2022_resistance_low_natural.tif.vat.cpg
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Dilts_etal_2022_resistance_low_natural.tif.vat.dbf
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Dilts_etal_2022_resistance_low_natural.tif.xml
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Dilts_etal_2022_resistance_low_nomargins.tfw
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Dilts_etal_2022_resistance_low_nomargins.tif
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Dilts_etal_2022_resistance_low_nomargins.tif.aux.xml
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Dilts_etal_2022_resistance_low_nomargins.tif.ovr
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Dilts_etal_2022_resistance_low_nomargins.tif.xml
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Dilts_etal_2022_resistance_medium_current.tfw
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Dilts_etal_2022_resistance_medium_current.tif
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Dilts_etal_2022_resistance_medium_current.tif.aux.xml
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Dilts_etal_2022_resistance_medium_current.tif.ovr
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Dilts_etal_2022_resistance_medium_current.tif.xml
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Dilts_etal_2022_resistance_medium_natural.tfw
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Dilts_etal_2022_resistance_medium_natural.tif
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Dilts_etal_2022_resistance_medium_natural.tif.aux.xml
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Dilts_etal_2022_resistance_medium_natural.tif.ovr
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Dilts_etal_2022_resistance_medium_natural.tif.vat.cpg
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Dilts_etal_2022_resistance_medium_natural.tif.vat.dbf
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Dilts_etal_2022_resistance_medium_natural.tif.xml
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Dilts_etal_2022_resistance_medium_nomargins.tfw
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Dilts_etal_2022_resistance_medium_nomargins.tif
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Dilts_etal_2022_resistance_medium_nomargins.tif.aux.xml
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Dilts_etal_2022_resistance_medium_nomargins.tif.ovr
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Dilts_etal_2022_resistance_medium_nomargins.tif.xml
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Dilts_etal_2022_study_area.cpg
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Dilts_etal_2022_study_area.dbf
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Dilts_etal_2022_study_area.prj
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Dilts_etal_2022_study_area.sbn
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Dilts_etal_2022_study_area.sbx
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Dilts_etal_2022_study_area.shp
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Dilts_etal_2022_study_area.shp.xml
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Dilts_etal_2022_study_area.shx
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Dilts_etal_2022_summary.csv
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README.txt
Abstract
One of the defining features of the Anthropocene is eroding ecosystem services as a function of decreases in biodiversity and overall reductions in the abundance of once-common organisms, including many insects that play innumerable roles in natural communities and agricultural systems that support human society. It is now clear that the preservation of insects cannot rely solely on the legal protection of natural areas far removed from the densest areas of human habitation. Instead, a critical challenge moving forward is to intelligently manage areas that include intensively farmed landscapes, such as the Central Valley of California. Here we attempt to meet this challenge with a tool for modeling landscape connectivity for insects (with pollinators in particular in mind) that builds on available information including lethality of pesticides and expert opinion on insect movement. Despite the massive fragmentation of the Central Valley, we find that connectivity is possible, especially utilizing the restoration or improvement of agricultural margins which (in their summed-area) exceed natural areas. Finally, we highlight steps moving forward and the great many knowledge gaps that could be addressed in the field to improve future iterations of our modeling approach.
Methods
We used publicly-available land cover data and reported pesticide application rates to develop resistance-to-movement scenarios for pollinating insects in the Central Valley of California, U.S.A. The primary land cover datasets were LandIQ, NOAA C-CAP, and the USGS National Land Cover dataset. Additional minor datasets included Normalized Difference Vegetation Index (NDVI) from National Agriculture Imagery Program 2016 and the California Protected Areas Database. These datasets were combined to create a single static land cover map covering the entire Central Valley at 30-meter resolution. Reported pesticide application rates were obtained from the California Department of Pesticide Report Pesticide Use Reporting (PUR) for 2104, 2015, and 2016. Pesticide application rates were converted to LD50s (lethal doses) and were further converted to resistance-to-movement to create maps of resistance-to-movement under low, medium, and high pesticide application rates. We further tested three agricultural margin scenarios: current margins, all margins converted to natural habitat, and all margins converted to the nearest crop type. We crossed the three margin scenarios with the three resistance surfaces in a factorial manner and generated least-cost paths connecting 205 points along the eastern margin of the Central Valley with 205 points on the western margin of the Central Valley. We assessed the following metrics derived from the least-cost paths: 1) total path length of least-cost paths connecting the western and eastern sides of the study area, 2) unique path length in the inner area, 3) average distance to the nearest path in the inner area, 4) the average number of paths in the inner area. We repeated this process for a close-up study area within the Central Valley in addition to the larger Central Valley.
Usage notes
Shapefiles and GeoTIFF files can be opened and viewed in a wide range of Geographic Information System software including ArcGIS, QGIS, R, GRASS, ArcGIS Online, etc. Vector geographic data is in shapefile format. The following file types are associated with shapefiles: cpg, dbf, prj, sbn, sbx, shp, shp.xml, shx. These files should not be separated from one another. If moved, they may cause the data to be unable to be displayed. Raster geographic data is in geotiff format. The following file types are associated with geotiff: tfw, tif, tif.aux.xml, tif.ovr, tif.vat.cpg, tif.vat.dbf, tif.xml. These files should not be separated from one another. If moved, they may cause the data to be unable to be displayed. The coordinate system for all geographic data is NAD 1983 UTM Zone 10N.