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NOTE: As of Mar 21, 2023 this record has been deprecated. Both source code and resulting data are now hosted in the same record (6014085). This record contains newer versions from what you can see in this record ------------- A Python port, including numerous model updates, of the Coastal Impacts and Adaptation Model, as described in Diaz 2016, along with the model inputs used in Diaz 2016. This code accompanies Depsky et al. 2022 (in prep.). See the manuscript for further details on model functionality and structure. See the Github repository for further documentation and the latest version of this code. pyCIAM, like CIAM, is a tool to estimate global economic impacts of sea level rise at fine resolution, accounting for adaptation and spatial variability and uncertainty in sea level rise. This model requires a number of socioeconomic and sea level rise inputs, organized by coastal "segment" and elevation. In Depsky et al. 2022, we develop the SLIIDERS datasets to serve this purpose; however, users may wish to alter and/or replace these datasets for their own purposes, especially if newer input data (used to generate SLIIDERS) becomes available.

NOTE: As of Mar 21, 2023 this record has been deprecated. Both source code and resulting data are now hosted in the same record (6014085). This record contains newer versions from what you can see in this record ------------- A Python port, including numerous model updates, of the Coastal Impacts and Adaptation Model, as described in Diaz 2016, along with the model inputs used in Diaz 2016. This code accompanies Depsky et al. 2022 (in prep.). See the manuscript for further details on model functionality and structure. See the Github repository for further documentation and the latest version of this code. pyCIAM, like CIAM, is a tool to estimate global economic impacts of sea level rise at fine resolution, accounting for adaptation and spatial variability and uncertainty in sea level rise. This model requires a number of socioeconomic and sea level rise inputs, organized by coastal "segment" and elevation. In Depsky et al. 2022, we develop the SLIIDERS datasets to serve this purpose; however, users may wish to alter and/or replace these datasets for their own purposes, especially if newer input data (used to generate SLIIDERS) becomes available.

Land use and climate alter species distributions worldwide, and detecting and understanding how species ranges shift can facilitate conservation planning and action. Following extirpation from most of the contiguous USA, gray wolves (Canis lupus) have partially recolonized former range in the western Great Lakes region, but it is unknown how land use and climate change may alter amounts of wolf habitat. Using wolf observation data collected during winters 2017–2020 in Minnesota, Wisconsin, and Michigan, we created ensemble models to predict how land use and climate change may affect the amount of wolf habitat within these states. A projection model for the western Great Lakes region suggested three of four scenarios of land use and climate change will lead to 9–35% increases in wolf habitat, while a solely climate-based projection model supported our expectation that changes in climate, in isolation, will have limited effect on current wolf range. Our results support stable or increasing amounts of wolf habitat in the western Great Lakes region during the 21st century, suggesting limited or no adverse effects on the current distribution or further recolonization of wolves. Our findings can inform policy development regarding wolf conservation, and identify areas where recolonization is plausible, thus where promoting human-wolf co-existence is most pertinent. The files '01_process_landuse_data.R' and '02_fit_landuse_model.R', along with supporting code in 'plot_functions.R', can be used to run the land use model in the paper. Data for this analysis includes wolf presence coordinates (at 0.25-degree resolution) for Michigan and Minnesota in the file 'wolf_presence_coordinates.csv'. Raw species presence data are not publicly available as the subject species (Gray wolf) is federally protected under the Endangered Species Act, and subject to poaching within the study area. Data for Wisconsin cannot be made publicly available due to legal restrictions, but data can be obtained by qualified researchers through the Wisconsin Department of Natural Resources (david.macfarland@wisconsin.gov). See the R files for information on how to download the LUH2 data. The file '03_fit_climate_model.R' fits the climate model and uses the historic wolf range shapefile found in the 'wolf_historic_range' folder. See the R file for information on how to download the corresponding climate data. All software required to run the code is open source and freely available. Running the models requires the R statistical software, along with libraries 'biomod2', 'dismo', 'dplyr', 'ggplot2', 'maps', 'ncdf4', 'raster', 'RStoolbox', 'sf', and 'tidyr'.

Land use and climate alter species distributions worldwide, and detecting and understanding how species ranges shift can facilitate conservation planning and action. Following extirpation from most of the contiguous USA, gray wolves (Canis lupus) have partially recolonized former range in the western Great Lakes region, but it is unknown how land use and climate change may alter amounts of wolf habitat. Using wolf observation data collected during winters 2017–2020 in Minnesota, Wisconsin, and Michigan, we created ensemble models to predict how land use and climate change may affect the amount of wolf habitat within these states. A projection model for the western Great Lakes region suggested three of four scenarios of land use and climate change will lead to 9–35% increases in wolf habitat, while a solely climate-based projection model supported our expectation that changes in climate, in isolation, will have limited effect on current wolf range. Our results support stable or increasing amounts of wolf habitat in the western Great Lakes region during the 21st century, suggesting limited or no adverse effects on the current distribution or further recolonization of wolves. Our findings can inform policy development regarding wolf conservation, and identify areas where recolonization is plausible, thus where promoting human-wolf co-existence is most pertinent. The files '01_process_landuse_data.R' and '02_fit_landuse_model.R', along with supporting code in 'plot_functions.R', can be used to run the land use model in the paper. Data for this analysis includes wolf presence coordinates (at 0.25-degree resolution) for Michigan and Minnesota in the file 'wolf_presence_coordinates.csv'. Raw species presence data are not publicly available as the subject species (Gray wolf) is federally protected under the Endangered Species Act, and subject to poaching within the study area. Data for Wisconsin cannot be made publicly available due to legal restrictions, but data can be obtained by qualified researchers through the Wisconsin Department of Natural Resources (david.macfarland@wisconsin.gov). See the R files for information on how to download the LUH2 data. The file '03_fit_climate_model.R' fits the climate model and uses the historic wolf range shapefile found in the 'wolf_historic_range' folder. See the R file for information on how to download the corresponding climate data. All software required to run the code is open source and freely available. Running the models requires the R statistical software, along with libraries 'biomod2', 'dismo', 'dplyr', 'ggplot2', 'maps', 'ncdf4', 'raster', 'RStoolbox', 'sf', and 'tidyr'.

We reconstructed an early-20th century ecological baseline using unique historical resources: (i) systematic bird surveys conducted by Joseph Grinnell from 1895–1904 at 71 sites around the Greater Los Angeles metropolitan area where he grew up and in the surrounding foothills (hereafter "Los Angeles"), and in California's Central Valley and surrounding foothills (hereafter "Central Valley") with colleagues in the early 1900's, and (ii) hand-digitized land-use maps from the same period. These were matched with contemporary bird resurveys and measures of land-use and climate change at the same sites. To obtain data on historic localities and bird species occurrences, we reviewed original field notebooks written by Joseph Grinnell and several of his colleagues, which are curated by the Museum of Vertebrate Zoology at UC Berkeley. These field notebooks provide detailed descriptions and maps of survey routes, as well as systematic lists of bird species observed each day. We identified 71 sites (43 Central Valley, 28 Los Angeles) with historic surveys of bird diversity that sampled representative habitats and climates throughout the range of each study region. Bird surveys by Grinnell and colleagues were carefully documented in the form of field notebooks, museum specimens, photographs of sampling sites, and annotated topographic maps. They were occasionally in the form of standardized abundance surveys that were precursors to modern line transects, more often as lists of species encountered each day that provide detection/non-detection data, and rarely as a daily list that identified only species that had not been detected previously. We conducted modern resurveys during the breeding seasons (April through July) of 2015–2017, matching the following characteristics of the historic surveys: geographical location and extent of survey sites, elevational range covered, habitats surveyed within sites, and timing of the survey during the breeding season. We used standardized variable-distance point counts along a 2.25 km transect, with 10 points placed 250 m apart, corresponding as closely as possible to the area and habitats noted by the historic surveyors and indicated by specimen collecting locations. Surveys began at dawn and lasted 2–3 hours. At each point along the transect, we recorded all birds seen or heard during a seven-minute period. Each site was surveyed daily over three consecutive days. Bird counts from modern surveys were collated for each day across all 10 points surveyed along a transect and reduced to detection/nondetection data per day per site for occupancy modeling. We used a dynamic multispecies occupancy model (described in the paper) to estimate the probabilities of occupancy, local colonization, and local persistence between the historic and modern survey periods. R scripts are provided. To characterize climate (long-term average weather conditions often presented as 30-year climate normals), data were obtained from 800 m resolution interpolated maps produced by the PRISM climate group and averaged over 30-year periods corresponding to the historic (1900–1929) and modern (1988–2017) surveys. To quantify land-use change (modern – historic), we created maps of historic land use within 1 km of our bird survey transects by hand‐digitizing historic maps using ArcMap for comparison with modern land-use data obtained from the National Land Cover Database (NLCD) at 100m, 200m, 500m and 1km distances from modern survey points. Water and urban area were hand-digitized from historic USGS topographic maps (ca. 1906–1932). Water bodies were directly outlined as polygons. Urban area was mapped as buildings (area of the building icon on the topographic map plus a buffer of 50 m) and roads (digitized as line features from the topographic map and given a width of 30 m). Historic agriculture was delineated using a series of three maps of irrigated lands in California in 1920. Climate and land-use change could exhibit concordant effects that favor or disfavor the same species, which would amplify their impacts, or species may respond to each threat in a divergent manner, causing opposing effects that moderate their impacts in isolation. We used early 20th-century surveys of birds conducted by Joseph Grinnell paired with modern resurveys and land-use change reconstructed from historic maps to examine avian change in Los Angeles and California's Central Valley (and their surrounding foothills). Occupancy and species richness declined greatly in Los Angeles from urbanization, strong warming (+1.8°C) and drying (-77.2 mm), but remained stable in the Central Valley, despite large-scale agricultural development, average warming (+0.9°C), and increased precipitation (+11.2 mm). While climate was the main driver of species distributions a century ago, the combined impacts of land-use and climate change drove temporal changes in occupancy, with similar numbers of species experiencing concordant and opposing effects. Excel and RFunding provided by: National Geographic SocietyCrossref Funder Registry ID: http://dx.doi.org/10.13039/100006363Award Number: 9972-16Funding provided by: Adolph C. and Mary Sprague Miller Institute for Basic Research in Science, University of California BerkeleyCrossref Funder Registry ID: http://dx.doi.org/10.13039/100007247Award Number: Funding provided by: UC Berkeley Chancellor's Fellowship*Crossref Funder Registry ID: Award Number: Funding provided by: National Science FoundationCrossref Funder Registry ID: http://dx.doi.org/10.13039/100000001Award Number: DEB 1457742Funding provided by: National Science FoundationCrossref Funder Registry ID: http://dx.doi.org/10.13039/100000001Award Number: 1911334Funding provided by: National Science FoundationCrossref Funder Registry ID: http://dx.doi.org/10.13039/100000001Award Number: 1601523

We reconstructed an early-20th century ecological baseline using unique historical resources: (i) systematic bird surveys conducted by Joseph Grinnell from 1895–1904 at 71 sites around the Greater Los Angeles metropolitan area where he grew up and in the surrounding foothills (hereafter "Los Angeles"), and in California's Central Valley and surrounding foothills (hereafter "Central Valley") with colleagues in the early 1900's, and (ii) hand-digitized land-use maps from the same period. These were matched with contemporary bird resurveys and measures of land-use and climate change at the same sites. To obtain data on historic localities and bird species occurrences, we reviewed original field notebooks written by Joseph Grinnell and several of his colleagues, which are curated by the Museum of Vertebrate Zoology at UC Berkeley. These field notebooks provide detailed descriptions and maps of survey routes, as well as systematic lists of bird species observed each day. We identified 71 sites (43 Central Valley, 28 Los Angeles) with historic surveys of bird diversity that sampled representative habitats and climates throughout the range of each study region. Bird surveys by Grinnell and colleagues were carefully documented in the form of field notebooks, museum specimens, photographs of sampling sites, and annotated topographic maps. They were occasionally in the form of standardized abundance surveys that were precursors to modern line transects, more often as lists of species encountered each day that provide detection/non-detection data, and rarely as a daily list that identified only species that had not been detected previously. We conducted modern resurveys during the breeding seasons (April through July) of 2015–2017, matching the following characteristics of the historic surveys: geographical location and extent of survey sites, elevational range covered, habitats surveyed within sites, and timing of the survey during the breeding season. We used standardized variable-distance point counts along a 2.25 km transect, with 10 points placed 250 m apart, corresponding as closely as possible to the area and habitats noted by the historic surveyors and indicated by specimen collecting locations. Surveys began at dawn and lasted 2–3 hours. At each point along the transect, we recorded all birds seen or heard during a seven-minute period. Each site was surveyed daily over three consecutive days. Bird counts from modern surveys were collated for each day across all 10 points surveyed along a transect and reduced to detection/nondetection data per day per site for occupancy modeling. We used a dynamic multispecies occupancy model (described in the paper) to estimate the probabilities of occupancy, local colonization, and local persistence between the historic and modern survey periods. R scripts are provided. To characterize climate (long-term average weather conditions often presented as 30-year climate normals), data were obtained from 800 m resolution interpolated maps produced by the PRISM climate group and averaged over 30-year periods corresponding to the historic (1900–1929) and modern (1988–2017) surveys. To quantify land-use change (modern – historic), we created maps of historic land use within 1 km of our bird survey transects by hand‐digitizing historic maps using ArcMap for comparison with modern land-use data obtained from the National Land Cover Database (NLCD) at 100m, 200m, 500m and 1km distances from modern survey points. Water and urban area were hand-digitized from historic USGS topographic maps (ca. 1906–1932). Water bodies were directly outlined as polygons. Urban area was mapped as buildings (area of the building icon on the topographic map plus a buffer of 50 m) and roads (digitized as line features from the topographic map and given a width of 30 m). Historic agriculture was delineated using a series of three maps of irrigated lands in California in 1920. Climate and land-use change could exhibit concordant effects that favor or disfavor the same species, which would amplify their impacts, or species may respond to each threat in a divergent manner, causing opposing effects that moderate their impacts in isolation. We used early 20th-century surveys of birds conducted by Joseph Grinnell paired with modern resurveys and land-use change reconstructed from historic maps to examine avian change in Los Angeles and California's Central Valley (and their surrounding foothills). Occupancy and species richness declined greatly in Los Angeles from urbanization, strong warming (+1.8°C) and drying (-77.2 mm), but remained stable in the Central Valley, despite large-scale agricultural development, average warming (+0.9°C), and increased precipitation (+11.2 mm). While climate was the main driver of species distributions a century ago, the combined impacts of land-use and climate change drove temporal changes in occupancy, with similar numbers of species experiencing concordant and opposing effects. Excel and RFunding provided by: National Geographic SocietyCrossref Funder Registry ID: http://dx.doi.org/10.13039/100006363Award Number: 9972-16Funding provided by: Adolph C. and Mary Sprague Miller Institute for Basic Research in Science, University of California BerkeleyCrossref Funder Registry ID: http://dx.doi.org/10.13039/100007247Award Number: Funding provided by: UC Berkeley Chancellor's Fellowship*Crossref Funder Registry ID: Award Number: Funding provided by: National Science FoundationCrossref Funder Registry ID: http://dx.doi.org/10.13039/100000001Award Number: DEB 1457742Funding provided by: National Science FoundationCrossref Funder Registry ID: http://dx.doi.org/10.13039/100000001Award Number: 1911334Funding provided by: National Science FoundationCrossref Funder Registry ID: http://dx.doi.org/10.13039/100000001Award Number: 1601523